In [ ]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import LabelEncoder
from scipy.stats import chi2_contingency,shapiro,spearmanr
from sklearn.preprocessing import StandardScaler,MinMaxScaler
#from sklearn.preprocessing import LabelEncode
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score,confusion_matrix, roc_curve, auc
from imblearn.over_sampling import SMOTE
Charger le dataset¶
In [ ]:
data= pd.read_excel("/content/Base_Donnees_AVC 1.xltm")
data.head()
Out[ ]:
| Genre | Age | Hypertension | Maladie_Cardiaque | Situation_Matrimoniale | Type_travail | Residence | Taux_glucose_moyen | IMC | Statut_Fumer | AVC | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Male | 67.0 | 0 | 1 | Yes | Private | Urban | 228.69 | 36.6 | formerly smoked | 1 |
| 1 | Female | 61.0 | 0 | 0 | Yes | Self-employed | Rural | 202.21 | NaN | never smoked | 1 |
| 2 | Male | 80.0 | 0 | 1 | Yes | Private | Rural | 105.92 | 32.5 | never smoked | 1 |
| 3 | Female | 49.0 | 0 | 0 | Yes | Private | Urban | 171.23 | 34.4 | smokes | 1 |
| 4 | Female | 79.0 | 1 | 0 | Yes | Self-employed | Rural | 174.12 | 24.0 | never smoked | 1 |
Contexte¶
Selon l'Organisation mondiale de la santé (OMS), l'accident vasculaire cérébral (AVC) est la deuxième cause de décès dans le monde, responsable d'environ 11 % du total des décès.¶
Ce jeu de données est utilisé pour prédire si un patient est susceptible de subir un accident vasculaire cérébral (AVC) en fonction de paramètres d'entrée tels que le sexe, l'âge, diverses maladies et le tabagisme. Chaque ligne des données fournit des informations pertinentes sur le patient.¶
Description des variables¶
1) Genre : "Homme = Male", "Femme = Female" ou "Autre".¶
2) Age : âge du patient¶
3) Hypertension : 0 si le patient ne souffre pas d'hypertension, 1 si le patient souffre d'hypertension.¶
4) Maladie_cardiaque : 0 si le patient n'a pas de maladie cardiaque, 1 si le patient a une maladie cardiaque.¶
5) Situation_matrimoniale : "Non = No" ou "Oui = Yes".¶
6) Type_travail : "enfants = children", "Govt_jov", "Never_worked", "Private" ou "Self-employed".¶
7) Residence : "Rural" ou "Urbain".¶
8) Taux_glucose_moyen : taux moyen de glucose dans le sang¶
9) IMC : indice de masse corporelle¶
10) Statut_fumer : "anciennement fumeur = formely smokes", "jamais fumeur = never smoke", "fumeur = smokes" ou "inconnu = unknown ".¶
11) AVC : 1 si le patient a eu un AVC ou 0 sinon¶
Petite Inspection¶
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# Dimension de la base de donnée
data.shape
Out[ ]:
(5110, 11)
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# Information de la base de donnée
data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 5110 entries, 0 to 5109 Data columns (total 11 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Genre 5110 non-null object 1 Age 5110 non-null float64 2 Hypertension 5110 non-null int64 3 Maladie_Cardiaque 5110 non-null int64 4 Situation_Matrimoniale 5110 non-null object 5 Type_travail 5110 non-null object 6 Residence 5110 non-null object 7 Taux_glucose_moyen 5110 non-null float64 8 IMC 4909 non-null float64 9 Statut_Fumer 5110 non-null object 10 AVC 5110 non-null int64 dtypes: float64(3), int64(3), object(5) memory usage: 439.3+ KB
Corriger quelques incoherences ou erreurs¶
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## Transformation de certains types de variables
data['Maladie_Cardiaque'] = data['Maladie_Cardiaque'].astype('category')
data['Hypertension'] = data['Hypertension'].astype('category')
data['AVC'] = data['AVC'].astype('category')
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data.duplicated().sum()
Out[ ]:
np.int64(0)
Il n'esxiste pas de duplication dans la base de donnée¶
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data.head()
Out[ ]:
| Genre | Age | Hypertension | Maladie_Cardiaque | Situation_Matrimoniale | Type_travail | Residence | Taux_glucose_moyen | IMC | Statut_Fumer | AVC | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Male | 67.0 | 0 | 1 | Yes | Private | Urban | 228.69 | 36.6 | formerly smoked | 1 |
| 1 | Female | 61.0 | 0 | 0 | Yes | Self-employed | Rural | 202.21 | NaN | never smoked | 1 |
| 2 | Male | 80.0 | 0 | 1 | Yes | Private | Rural | 105.92 | 32.5 | never smoked | 1 |
| 3 | Female | 49.0 | 0 | 0 | Yes | Private | Urban | 171.23 | 34.4 | smokes | 1 |
| 4 | Female | 79.0 | 1 | 0 | Yes | Self-employed | Rural | 174.12 | 24.0 | never smoked | 1 |
In [ ]:
data['AVC'].value_counts()
Out[ ]:
| count | |
|---|---|
| AVC | |
| 0 | 4861 |
| 1 | 249 |
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data['Statut_Fumer'].value_counts()
Out[ ]:
| count | |
|---|---|
| Statut_Fumer | |
| never smoked | 1892 |
| Unknown | 1544 |
| formerly smoked | 885 |
| smokes | 789 |
Pour la variable statut fumer ya des valeurs non renseigner nous allons les transformer en NAN ensuite les traiter¶
In [ ]:
## Valeur manquante
data.isnull().sum()
Out[ ]:
| 0 | |
|---|---|
| Genre | 0 |
| Age | 0 |
| Hypertension | 0 |
| Maladie_Cardiaque | 0 |
| Situation_Matrimoniale | 0 |
| Type_travail | 0 |
| Residence | 0 |
| Taux_glucose_moyen | 0 |
| IMC | 201 |
| Statut_Fumer | 0 |
| AVC | 0 |
Il existe des valeurs manquantes sur les variables IMC et le statut des fumeur¶
Analyse exploratoire Avant prétraitement des données¶
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# Séparation des variables selon leur type
data_numeriques = data.select_dtypes(include=['int64', 'float64'])
data_categorielles = data.select_dtypes(include=['object', 'category'])
Pour les variables numériques¶
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## Résume descriptif
data.describe(include="number").T
Out[ ]:
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| Age | 5110.0 | 43.226614 | 22.612647 | 0.08 | 25.000 | 45.000 | 61.00 | 82.00 |
| Taux_glucose_moyen | 5110.0 | 106.147677 | 45.283560 | 55.12 | 77.245 | 91.885 | 114.09 | 271.74 |
| IMC | 4909.0 | 28.893237 | 7.854067 | 10.30 | 23.500 | 28.100 | 33.10 | 97.60 |
In [ ]:
plt.figure(figsize=(14,4))
for col in data_numeriques.columns:
plt.subplot(121)
sns.set_theme(style="darkgrid")
sns.histplot(data=data,x=col,kde=True)
plt.title(f'Histogramme de la variable {col}')
plt.subplot(122)
sns.boxplot(data[col])
plt.title(f'Boxplot de la variable {col}')
plt.show()
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for col in data_numeriques.columns:
print(f"Skewness de la variable {col}: {data[col].skew():.2f}")
print(f"Kurtosis de la variable {col}: {data[col].kurt():.2f}")
Skewness de la variable Age: -0.14 Kurtosis de la variable Age: -0.99 Skewness de la variable Taux_glucose_moyen: 1.57 Kurtosis de la variable Taux_glucose_moyen: 1.68 Skewness de la variable IMC: 1.06 Kurtosis de la variable IMC: 3.36
test de normalité (Shapiro-Wilk)¶
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for col in data_numeriques.columns:
stat, p_value = shapiro(data[col].sample(500, random_state=42))
if p_value < 0.05:
print(f"La variable {col} ne suit pas la loi normale (p-value = {p_value})")
else:
print(f"La variable {col} suit la loi normale (p-value = {p_value})")
La variable Age ne suit pas la loi normale (p-value = 6.400304511076399e-09) La variable Taux_glucose_moyen ne suit pas la loi normale (p-value = 2.3073166356644035e-25) La variable IMC suit la loi normale (p-value = nan)
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for col in data_numeriques.columns:
stat, p_value = shapiro(data[col])
if p_value < 0.05:
print(f"La variable {col} ne suit pas la loi normale (p-value = {p_value})")
else:
print(f"La variable {col} suit la loi normale (p-value = {p_value})")
La variable Age ne suit pas la loi normale (p-value = 1.3789355302240572e-32) La variable Taux_glucose_moyen ne suit pas la loi normale (p-value = 1.795389063729762e-61) La variable IMC suit la loi normale (p-value = nan)
/usr/local/lib/python3.11/dist-packages/scipy/stats/_axis_nan_policy.py:586: UserWarning: scipy.stats.shapiro: For N > 5000, computed p-value may not be accurate. Current N is 5110. res = hypotest_fun_out(*samples, **kwds)
Pour les variables catégorielles¶
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plt.figure(figsize=(5, 24))
sns.set_style("whitegrid", {'grid.linestyle': ':', 'axes.edgecolor': '0.4'})
for i, col in enumerate(data_categorielles.columns, 1):
plt.subplot(len(data_categorielles.columns), 1, i)
# Tri par fréquence
order = data_categorielles[col].value_counts(dropna=False).index
ax = sns.countplot(
x=col,
data=data_categorielles,
hue = "AVC",
order=order,
palette='plasma_r',
edgecolor='black',
linewidth=0.5,
saturation=0.9
)
# Annotations optimisées
for p in ax.patches:
height = p.get_height()
ax.text(
p.get_x() + p.get_width()/2,
height + max(height*0.05, 0.5), # Adaptation dynamique à la hauteur
f'{int(height)}',
ha='center',
va='bottom',
fontsize=9,
color='black',
bbox=dict(facecolor='white', alpha=0.8, edgecolor='none', boxstyle='round,pad=0.2')
)
# Paramètres esthétiques
plt.title(f'Distribution de {col}', pad=12, fontsize=13, fontweight='semibold')
plt.ylabel('Fréquence', fontsize=10)
plt.xlabel('', fontsize=0) # Suppression totale de l'axe X
# Rotation et alignement des ticks
plt.xticks(
rotation=45,
ha='right',
fontsize=9,
rotation_mode='anchor' # Meilleur alignement
)
# Décoration des axes
ax.spines[['top', 'right']].set_visible(False)
ax.grid(axis='y', linestyle=':', alpha=0.6)
plt.tight_layout(pad=2.0) # Réduction de l'espacement
plt.subplots_adjust(hspace=1) # Espace vertical entre les subplots
plt.show()
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# prompt: met en commentaire la cellule precedente
#import matplotlib.pyplot as plt
#plt.figure(figsize=(20, 16))
#sns.set_style("whitegrid", {'grid.linestyle': ':', 'axes.edgecolor': '0.4'})
## Dictionnaire de renommage des variables
#noms_variables = {
# 'Hypertension': 'Hypertension', # Renommer Hypertension
# 'AVC': 'Accident Vasculaire Cérébral',
# 'Maladie_Cardiaque': 'Maladie Cardiaque', # Renommer Maladie_Cardiaque
# 'Situation_Matrimoniale': 'Situation Matrimoniale'
#}
## Dictionnaire de renommage des modalités
#noms_modalites = {
# 0: 'Non',
# 1: 'Oui',
# '0': 'Non',
# '1': 'Oui',
#
#}
## Variables à traiter spécifiquement (en utilisant les noms de colonnes originaux)
#variables_binaires = ['Hypertension', 'AVC', 'Maladie_Cardiaque']
## Disposition des graphiques
#n_cols = 4
#n_rows = (len(data_categorielles.columns) + n_cols - 1) // n_cols
#for i, col in enumerate(data_categorielles.columns, 1):
# plt.subplot(n_rows, n_cols, i)
#
# # Copie des données avec conversion des modalités
# temp_data = data_categorielles.copy()
#
# # Application du renommage pour les variables binaires
# if col in variables_binaires:
# # Assurez-vous que la colonne est du type str avant de remplacer les modalités
# temp_data[col] = temp_data[col].astype(str).replace(noms_modalites)
#
# # Tri par fréquence
# order = temp_data[col].value_counts(dropna=False).index
#
# # Gestion de la variable hue (AVC) - utilisez le nom de colonne original
# hue_var = "AVC" if col != "AVC" else None
# if hue_var:
# # Appliquer le renommage des modalités au hue_var
# temp_data[hue_var] = temp_data[hue_var].astype(str).replace(noms_modalites)
#
# # Création du graphique
# ax = sns.countplot(
# x=col,
# data=temp_data,
# hue=hue_var, # Utiliser le nom de colonne original
# order=order,
# palette='plasma_r',
# edgecolor='black',
# linewidth=0.5,
# saturation=0.9
# )
# # Annotations
# for p in ax.patches:
# height = p.get_height()
# ax.text(
# p.get_x() + p.get_width()/2,
# height + max(height*0.05, 0.5),
# f'{int(height)}',
# ha='center',
# va='bottom',
# fontsize=9,
# color='black',
# bbox=dict(facecolor='white', alpha=0.8, edgecolor='none', boxstyle='round,pad=0.2')
# )
# # Titre avec nom de variable renommé
# title = noms_variables.get(col, col) # Utiliser le nom de colonne original pour chercher dans le dict
# plt.title(f'Distribution de {title}', pad=12, fontsize=13, fontweight='semibold')
#
# # Axes
# plt.ylabel('Fréquence', fontsize=10)
# plt.xlabel('')
# plt.xticks(rotation=45, ha='right', fontsize=9, rotation_mode='anchor')
#
# # Légende
# if ax.legend_:
# handles, labels = ax.get_legend_handles_labels()
# # Appliquer le renommage des modalités aux labels de la légende
# new_labels = [noms_modalites.get(l, l) for l in labels]
# ax.legend(handles, new_labels, title=noms_variables.get("AVC", "AVC")) # Utiliser le nom de colonne original pour le titre de la légende
# ax.spines[['top', 'right']].set_visible(False)
# ax.grid(axis='y', linestyle=':', alpha=0.6)
## Ajustement final
#plt.tight_layout(pad=3.0)
#plt.subplots_adjust(hspace=0.5, wspace=0.3)
#plt.show()
In [ ]:
In [ ]:
def analyse_variable_categorielle(col, df=data, rotate_xticks=True, max_categories=10):
# Calcul des effectifs et pourcentages
freq_table = df[col].value_counts()
percent_table = round(freq_table / freq_table.sum() * 100, 2)
stats_df = pd.DataFrame({'Effectifs': freq_table, 'Pourcentages (%)': percent_table})
# Affichage du tableau
print(f"\n--- {col.upper()} ---")
display(stats_df)
# Ajustement du nombre de catégories affichées
top_categories = freq_table[:max_categories]
# Affichage du tableau
print(f"\n--- {col.upper()} ---")
display(stats_df)
# Ajustement du nombre de catégories affichées
top_categories = freq_table[:max_categories]
In [ ]:
for var in data_categorielles:
analyse_variable_categorielle(var)
--- GENRE ---
| Effectifs | Pourcentages (%) | |
|---|---|---|
| Genre | ||
| Female | 2994 | 58.59 |
| Male | 2115 | 41.39 |
| Other | 1 | 0.02 |
--- GENRE ---
| Effectifs | Pourcentages (%) | |
|---|---|---|
| Genre | ||
| Female | 2994 | 58.59 |
| Male | 2115 | 41.39 |
| Other | 1 | 0.02 |
--- HYPERTENSION ---
| Effectifs | Pourcentages (%) | |
|---|---|---|
| Hypertension | ||
| 0 | 4612 | 90.25 |
| 1 | 498 | 9.75 |
--- HYPERTENSION ---
| Effectifs | Pourcentages (%) | |
|---|---|---|
| Hypertension | ||
| 0 | 4612 | 90.25 |
| 1 | 498 | 9.75 |
--- MALADIE_CARDIAQUE ---
| Effectifs | Pourcentages (%) | |
|---|---|---|
| Maladie_Cardiaque | ||
| 0 | 4834 | 94.6 |
| 1 | 276 | 5.4 |
--- MALADIE_CARDIAQUE ---
| Effectifs | Pourcentages (%) | |
|---|---|---|
| Maladie_Cardiaque | ||
| 0 | 4834 | 94.6 |
| 1 | 276 | 5.4 |
--- SITUATION_MATRIMONIALE ---
| Effectifs | Pourcentages (%) | |
|---|---|---|
| Situation_Matrimoniale | ||
| Yes | 3353 | 65.62 |
| No | 1757 | 34.38 |
--- SITUATION_MATRIMONIALE ---
| Effectifs | Pourcentages (%) | |
|---|---|---|
| Situation_Matrimoniale | ||
| Yes | 3353 | 65.62 |
| No | 1757 | 34.38 |
--- TYPE_TRAVAIL ---
| Effectifs | Pourcentages (%) | |
|---|---|---|
| Type_travail | ||
| Private | 2925 | 57.24 |
| Self-employed | 819 | 16.03 |
| children | 687 | 13.44 |
| Govt_job | 657 | 12.86 |
| Never_worked | 22 | 0.43 |
--- TYPE_TRAVAIL ---
| Effectifs | Pourcentages (%) | |
|---|---|---|
| Type_travail | ||
| Private | 2925 | 57.24 |
| Self-employed | 819 | 16.03 |
| children | 687 | 13.44 |
| Govt_job | 657 | 12.86 |
| Never_worked | 22 | 0.43 |
--- RESIDENCE ---
| Effectifs | Pourcentages (%) | |
|---|---|---|
| Residence | ||
| Urban | 2596 | 50.8 |
| Rural | 2514 | 49.2 |
--- RESIDENCE ---
| Effectifs | Pourcentages (%) | |
|---|---|---|
| Residence | ||
| Urban | 2596 | 50.8 |
| Rural | 2514 | 49.2 |
--- STATUT_FUMER ---
| Effectifs | Pourcentages (%) | |
|---|---|---|
| Statut_Fumer | ||
| never smoked | 1892 | 37.03 |
| Unknown | 1544 | 30.22 |
| formerly smoked | 885 | 17.32 |
| smokes | 789 | 15.44 |
--- STATUT_FUMER ---
| Effectifs | Pourcentages (%) | |
|---|---|---|
| Statut_Fumer | ||
| never smoked | 1892 | 37.03 |
| Unknown | 1544 | 30.22 |
| formerly smoked | 885 | 17.32 |
| smokes | 789 | 15.44 |
--- AVC ---
| Effectifs | Pourcentages (%) | |
|---|---|---|
| AVC | ||
| 0 | 4861 | 95.13 |
| 1 | 249 | 4.87 |
--- AVC ---
| Effectifs | Pourcentages (%) | |
|---|---|---|
| AVC | ||
| 0 | 4861 | 95.13 |
| 1 | 249 | 4.87 |
In [ ]:
# Création des classes d'âge
#bins = [0, 30, 60, data['Age'].max()]
#labels = ['0-45', '46-60', f"61-{int(data['Age'].max())}"]
#data['Age_Classe'] = pd.cut(data['Age'], bins=bins, labels=labels, right=True, include_lowest=True)
# Calcul de la table de fréquence et de pourcentage pour l'âge classé
#age_freq = data['Age_Classe'].value_counts()
#age_percent = (age_freq / age_freq.sum() * 100).round(2)
# Création du DataFrame pour le tableau
#age_table = pd.DataFrame({'Effectifs': age_freq, 'Pourcentages (%)': age_percent})
# Affichage du tableau
#print("\n--- Distribution de l'Âge par Classe ---")
#display(age_table)
Préprocessing¶
Gestion des valeurs manquantes¶
Pour la variable IMC¶
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# Calcul de l'écart-type pour la colonne IMC
ecart_type = data['IMC'].std()
print(f"Écart-type de l'IMC : {ecart_type:.2f}")
Écart-type de l'IMC : 7.85
Nous constatons que la distribution de la variable IMC suit la loi normal et que l'ecart types n'est pas grand alors la série n'est pas trop dispersé autour de sa moyenne.De plus la moyenne est sensiblement égale à la médiane alors la moyenne peut être un bon indicateur pour l'imputation¶
In [ ]:
## Imputation par la moyenne
moy = data['IMC'].mean()
data['IMC'] = data['IMC'].fillna(moy)
Pour la variable statut fumeur¶
In [ ]:
data['Statut_Fumer'] = data['Statut_Fumer'].replace('Unknown', np.nan)
data['Statut_Fumer'].value_counts()
Out[ ]:
| count | |
|---|---|
| Statut_Fumer | |
| never smoked | 1892 |
| formerly smoked | 885 |
| smokes | 789 |
In [ ]:
data.isnull().sum()
Out[ ]:
| 0 | |
|---|---|
| Genre | 0 |
| Age | 0 |
| Hypertension | 0 |
| Maladie_Cardiaque | 0 |
| Situation_Matrimoniale | 0 |
| Type_travail | 0 |
| Residence | 0 |
| Taux_glucose_moyen | 0 |
| IMC | 0 |
| Statut_Fumer | 1544 |
| AVC | 0 |
In [ ]:
# Calculer le mode pour cette variables
mode = data['Statut_Fumer'].mode()[0]
print(f"Mode de la variable statut fumeur : {mode}")
Mode de la variable statut fumeur : never smoked
In [ ]:
## Imputation par le mode
data['Statut_Fumer'] = data['Statut_Fumer'].fillna(mode)
In [ ]:
data.isnull().sum()
Out[ ]:
| 0 | |
|---|---|
| Genre | 0 |
| Age | 0 |
| Hypertension | 0 |
| Maladie_Cardiaque | 0 |
| Situation_Matrimoniale | 0 |
| Type_travail | 0 |
| Residence | 0 |
| Taux_glucose_moyen | 0 |
| IMC | 0 |
| Statut_Fumer | 0 |
| AVC | 0 |
Gestion des valeurs abberantes¶
In [ ]:
def replace_outliers_in_columns(df, columns, method="median"):
df_cleaned = df.copy()
for col in columns:
Q1 = df[col].quantile(0.25)
Q3 = df[col].quantile(0.75)
IQR = Q3 - Q1
lower = Q1 - 1.5 * IQR
upper = Q3 + 1.5 * IQR
if method == "median":
replacement_value = df[col].median()
elif method == "mean":
replacement_value = df[col].mean()
if method in ["median", "mean"]:
df_cleaned[col] = df[col].apply(
lambda x: replacement_value if x < lower or x > upper else x
)
elif method == "bounds":
df_cleaned[col] = df[col].apply(
lambda x: lower if x < lower else upper if x > upper else x
)
else:
raise ValueError("Méthode inconnue : utiliser 'mean', 'median' ou 'bounds'")
return df_cleaned
In [ ]:
df= replace_outliers_in_columns(data, ["Age", "Taux_glucose_moyen", "IMC" ], method="bounds")
Analyse exploratoire aprés prétraitement des données¶
Analyse univarié¶
In [ ]:
## Résumé descriptif
df.describe().T
Out[ ]:
| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| Age | 5110.0 | 43.226614 | 22.612647 | 0.08 | 25.000 | 45.000 | 61.00 | 82.0000 |
| Taux_glucose_moyen | 5110.0 | 100.996204 | 33.214738 | 55.12 | 77.245 | 91.885 | 114.09 | 169.3575 |
| IMC | 5110.0 | 28.721613 | 7.119940 | 10.30 | 23.800 | 28.400 | 32.80 | 46.3000 |
In [ ]:
#Séparation des variables
df_numeriques = df.select_dtypes(include=['int64', 'float64'])
df_categorielles = df.select_dtypes(include=['object', 'category'])
Pour les variables numériques¶
In [ ]:
plt.figure(figsize=(14,4))
for col in df_numeriques.columns:
plt.subplot(121)
sns.histplot(data=df,x=col,kde=True)
plt.title(f'Histogramme de la variable {col}')
plt.subplot(122)
sns.boxplot(df[col])
plt.title(f'Boxplot de la variable {col}')
plt.show()
In [ ]:
for col in df_numeriques.columns:
print(f"Skewness de la variable {col}: {df[col].skew():.2f}")
print(f"Kurtosis de la variable {col}: {df[col].kurt():.2f}")
Skewness de la variable Age: -0.14 Kurtosis de la variable Age: -0.99 Skewness de la variable Taux_glucose_moyen: 0.94 Kurtosis de la variable Taux_glucose_moyen: -0.17 Skewness de la variable IMC: 0.44 Kurtosis de la variable IMC: -0.08
Test de normalité¶
In [ ]:
for col in df_numeriques.columns:
stat, p_value = shapiro(df[col].sample(500, random_state=42))
if p_value < 0.05:
print(f"La variable {col} ne suit pas la loi normale (p-value = {p_value})")
else:
print(f"La variable {col} suit la loi normale (p-value = {p_value})")
La variable Age ne suit pas la loi normale (p-value = 6.400304511076399e-09) La variable Taux_glucose_moyen ne suit pas la loi normale (p-value = 3.115839080369874e-20) La variable IMC ne suit pas la loi normale (p-value = 7.104313222108549e-07)
Pour les variables catégorielles¶
In [ ]:
# Tableau de résumé
df.describe(include=['category', 'object']).T
Out[ ]:
| count | unique | top | freq | |
|---|---|---|---|---|
| Genre | 5110 | 3 | Female | 2994 |
| Hypertension | 5110 | 2 | 0 | 4612 |
| Maladie_Cardiaque | 5110 | 2 | 0 | 4834 |
| Situation_Matrimoniale | 5110 | 2 | Yes | 3353 |
| Type_travail | 5110 | 5 | Private | 2925 |
| Residence | 5110 | 2 | Urban | 2596 |
| Statut_Fumer | 5110 | 3 | never smoked | 3436 |
| AVC | 5110 | 2 | 0 | 4861 |
In [ ]:
plt.figure(figsize=(5, 24))
sns.set_style("whitegrid", {'grid.linestyle': ':', 'axes.edgecolor': '0.4'})
for i, col in enumerate(df_categorielles.columns, 1):
plt.subplot(len(df_categorielles.columns), 1, i)
# Tri par fréquence
order = df_categorielles[col].value_counts(dropna=False).index
ax = sns.countplot(
x=col,
data=df_categorielles,
order=order,
palette='plasma_r',
edgecolor='black',
linewidth=0.5,
saturation=0.9
)
# Annotations optimisées
for p in ax.patches:
height = p.get_height()
ax.text(
p.get_x() + p.get_width()/2,
height + max(height*0.05, 0.5), # Adaptation dynamique à la hauteur
f'{int(height)}',
ha='center',
va='bottom',
fontsize=9,
color='black',
bbox=dict(facecolor='white', alpha=0.8, edgecolor='none', boxstyle='round,pad=0.2')
)
# Paramètres esthétiques
plt.title(f'Distribution de {col}', pad=12, fontsize=13, fontweight='semibold')
plt.ylabel('Fréquence', fontsize=10)
plt.xlabel('', fontsize=0) # Suppression totale de l'axe X
# Rotation et alignement des ticks
plt.xticks(
rotation=45,
ha='right',
fontsize=9,
rotation_mode='anchor' # Meilleur alignement
)
# Décoration des axes
ax.spines[['top', 'right']].set_visible(False)
ax.grid(axis='y', linestyle=':', alpha=0.6)
plt.tight_layout(pad=2.0) # Réduction de l'espacement
plt.subplots_adjust(hspace=0.8) # Espace vertical entre les subplots
plt.show()
/tmp/ipython-input-39-2017314915.py:10: FutureWarning: Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect. ax = sns.countplot( /tmp/ipython-input-39-2017314915.py:10: FutureWarning: Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect. ax = sns.countplot( /tmp/ipython-input-39-2017314915.py:10: FutureWarning: Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect. ax = sns.countplot( /tmp/ipython-input-39-2017314915.py:10: FutureWarning: Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect. ax = sns.countplot( /tmp/ipython-input-39-2017314915.py:10: FutureWarning: Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect. ax = sns.countplot( /tmp/ipython-input-39-2017314915.py:10: FutureWarning: Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect. ax = sns.countplot( /tmp/ipython-input-39-2017314915.py:10: FutureWarning: Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect. ax = sns.countplot( /tmp/ipython-input-39-2017314915.py:10: FutureWarning: Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect. ax = sns.countplot(
Analyse descriptive bivariée¶
Entre Variable quantitative¶
Nuage de point¶
In [ ]:
# pair plot des variables continues
plt.figure(figsize = (10,10))
sns.pairplot(data = df,hue = "AVC",diag_kind = 'hist')
plt.suptitle('Pairplot des variables continues', y=1.02)
plt.show( )
<Figure size 1000x1000 with 0 Axes>
Corrélations entre variables numériques¶
In [ ]:
# Calcul de la matrice de corrélation
corr_matrix = df_numeriques.corr()
# Affichage du heatmap
plt.figure(figsize=(8, 6))
sns.heatmap(corr_matrix, annot=True, cmap="coolwarm", fmt=".2f", linewidths=0.5, linecolor='gray')
plt.title("📌 Corrélation entre variables numériques", fontsize=14)
plt.xticks(rotation=45)
plt.yticks(rotation=0)
plt.tight_layout()
plt.show()
/tmp/ipython-input-41-2124487701.py:10: UserWarning: Glyph 128204 (\N{PUSHPIN}) missing from font(s) DejaVu Sans.
plt.tight_layout()
/usr/local/lib/python3.11/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 128204 (\N{PUSHPIN}) missing from font(s) DejaVu Sans.
fig.canvas.print_figure(bytes_io, **kw)
Test de corrélation (spearman)¶
In [ ]:
def compute_significant_spearman_correlations(data, alpha=0.05):
df_numeriquesss = data.select_dtypes(include=["int64", "float64"]).columns
results = []
for i, col1 in enumerate(df_numeriquesss):
for col2 in df_numeriquesss[i+1:]:
corr, p = spearmanr(data[col1], data[col2])
if p < alpha:
results.append({
"Variable 1": col1,
"Variable 2": col2,
"Corrélation": round(corr, 2),
"p-value": round(p, 4)
})
elif p >= alpha: # Ajout du elif pour inclure toutes les corrélations
results.append({
"Variable 1": col1,
"Variable 2": col2,
"Corrélation": round(corr, 2),
"p-value": round(p, 4)})
return pd.DataFrame(results).sort_values(by="Corrélation", key=abs, ascending=False)
In [ ]:
# Appel à la fonction apllique à nos df
significant_spearman_corrs = compute_significant_spearman_correlations(df_numeriques)
display(significant_spearman_corrs)
| Variable 1 | Variable 2 | Corrélation | p-value | |
|---|---|---|---|---|
| 1 | Age | IMC | 0.36 | 0.0 |
| 0 | Age | Taux_glucose_moyen | 0.14 | 0.0 |
| 2 | Taux_glucose_moyen | IMC | 0.11 | 0.0 |
Entre variable catégorielle¶
In [ ]:
cat_vars = ['Genre', 'Hypertension', 'Maladie_Cardiaque', 'Situation_Matrimoniale',
'Type_travail', 'Residence', 'Statut_Fumer']
plt.figure(figsize=(16, 10)) # Ajustez la hauteur selon vos besoins
# Première ligne avec 4 graphiques
for i, var in enumerate(cat_vars[:4], 1):
plt.subplot(2, 4, i) # 2 lignes, 4 colonnes, position i
sns.countplot(data=df, x='AVC', hue=var, palette='plasma_r',
edgecolor='black',
linewidth=0.5,
saturation=0.9)
plt.title(f"Répartition de AVC selon {var}")
plt.xlabel("AVC")
plt.ylabel("Nombre d'observations")
plt.legend(title=var, loc='upper right')
# Deuxième ligne avec 3 graphiques (centrés)
for i, var in enumerate(cat_vars[4:], 5): # Commence à la position 5
plt.subplot(2, 4, i) # 2 lignes, 4 colonnes, positions 5,6,7
sns.countplot(data=df, x='AVC', hue=var, palette='plasma_r',
edgecolor='black',
linewidth=0.5,
saturation=0.9)
plt.title(f"Répartition de AVC selon {var}")
plt.xlabel("AVC")
plt.ylabel("Nombre d'observations")
plt.legend(title=var, loc='upper right')
# Masquer le dernier subplot (8ème position) s'il n'est pas utilisé
if len(cat_vars) == 7:
plt.subplot(2, 4, 8)
plt.axis('off')
plt.tight_layout()
plt.show()
In [ ]:
cat_vars = ['Genre', 'Hypertension', 'Maladie_Cardiaque', 'Situation_Matrimoniale',
'Type_travail', 'Residence', 'Statut_Fumer']
# Création de la figure avec une grille 2x4
plt.figure(figsize=(18, 12))
# Première ligne: 4 graphiques
for i, var in enumerate(cat_vars[:4], 1):
plt.subplot(2, 4, i)
ax = sns.countplot(data=df, x='AVC', hue=var, palette='plasma_r',
edgecolor='black', linewidth=0.5, saturation=0.9)
plt.title(f"AVC par {var}", fontsize=12, pad=10)
plt.xlabel("AVC", fontsize=10)
plt.ylabel("Count", fontsize=10)
plt.legend(title=var, bbox_to_anchor=(1.05, 1), title_fontsize=9)
# Annotations optimisées
for p in ax.patches:
height = p.get_height()
ax.text(
p.get_x() + p.get_width()/2,
height + max(height*0.05, 0.5), # Adaptation dynamique à la hauteur
f'{int(height)}',
ha='center',
va='bottom',
fontsize=9,
color='black',
bbox=dict(facecolor='white', alpha=0.8, edgecolor='none', boxstyle='round,pad=0.2')
)
# Deuxième ligne: 3 graphiques centrés
for i, var in enumerate(cat_vars[4:], 5):
plt.subplot(2, 4, i)
ax = sns.countplot(data=df, x='AVC', hue=var, palette='plasma_r',
edgecolor='black', linewidth=0.5, saturation=0.9)
plt.title(f"AVC par {var}", fontsize=12, pad=10)
plt.xlabel("AVC", fontsize=10)
plt.ylabel("Count", fontsize=10)
plt.legend(title=var, bbox_to_anchor=(1.05, 1), title_fontsize=9)
# Annotations optimisées
for p in ax.patches:
height = p.get_height()
ax.text(
p.get_x() + p.get_width()/2,
height + max(height*0.05, 0.5), # Adaptation dynamique à la hauteur
f'{int(height)}',
ha='center',
va='bottom',
fontsize=9,
color='black',
bbox=dict(facecolor='white', alpha=0.8, edgecolor='none', boxstyle='round,pad=0.2')
)
# Désactiver le 8ème subplot
plt.subplot(2, 4, 8)
plt.axis('off')
# Ajustement de l'espacement
plt.tight_layout(pad=3.0)
plt.suptitle("Répartition des cas d'AVC selon différentes caractéristiques", y=1.03, fontsize=14, weight='bold')
plt.show()
Test d'independance de khi-deux¶
In [ ]:
for var in df_categorielles.columns[:-1]:
table_contegence = pd.crosstab(df[var], df['AVC'])
print(f"Tableau croisé pour {var}:\n", table_contegence)
# Test du Chi-carré
chi2, p, dof, expected = chi2_contingency(table_contegence)
print(f"Test du Chi-carré pour {var}:")
print(f" Chi2 statistic: {chi2}")
print(f" p-value: {p}")
if p < 0.05:
print(f"il existe une relation significative entre {var} et le type AVC.\n\n\n")
else:
print(f"Aucune relation significative détecté entre {var} et le type AVC.\n\n")
Tableau croisé pour Genre: AVC 0 1 Genre Female 2853 141 Male 2007 108 Other 1 0 Test du Chi-carré pour Genre: Chi2 statistic: 0.47258662884530234 p-value: 0.7895490538408245 Aucune relation significative détecté entre Genre et le type AVC. Tableau croisé pour Hypertension: AVC 0 1 Hypertension 0 4429 183 1 432 66 Test du Chi-carré pour Hypertension: Chi2 statistic: 81.6053682482931 p-value: 1.661621901511823e-19 il existe une relation significative entre Hypertension et le type AVC. Tableau croisé pour Maladie_Cardiaque: AVC 0 1 Maladie_Cardiaque 0 4632 202 1 229 47 Test du Chi-carré pour Maladie_Cardiaque: Chi2 statistic: 90.25956125843324 p-value: 2.0887845685229236e-21 il existe une relation significative entre Maladie_Cardiaque et le type AVC. Tableau croisé pour Situation_Matrimoniale: AVC 0 1 Situation_Matrimoniale No 1728 29 Yes 3133 220 Test du Chi-carré pour Situation_Matrimoniale: Chi2 statistic: 58.923890259034195 p-value: 1.6389021142314745e-14 il existe une relation significative entre Situation_Matrimoniale et le type AVC. Tableau croisé pour Type_travail: AVC 0 1 Type_travail Govt_job 624 33 Never_worked 22 0 Private 2776 149 Self-employed 754 65 children 685 2 Test du Chi-carré pour Type_travail: Chi2 statistic: 49.163511976675295 p-value: 5.397707801896119e-10 il existe une relation significative entre Type_travail et le type AVC. Tableau croisé pour Residence: AVC 0 1 Residence Rural 2400 114 Urban 2461 135 Test du Chi-carré pour Residence: Chi2 statistic: 1.0816367471627524 p-value: 0.29833169286876987 Aucune relation significative détecté entre Residence et le type AVC. Tableau croisé pour Statut_Fumer: AVC 0 1 Statut_Fumer formerly smoked 815 70 never smoked 3299 137 smokes 747 42 Test du Chi-carré pour Statut_Fumer: Chi2 statistic: 23.766301101998046 p-value: 6.905788734861923e-06 il existe une relation significative entre Statut_Fumer et le type AVC.
Entre variable quantitative et qualitative¶
In [ ]:
print(df.groupby('Genre')['Age'].describe())
# Boxplot
sns.boxplot(x='Genre', y='Age', data=df, palette="Set2")
plt.title("🛑 L'age selon le genre")
plt.show()
count mean std min 25% 50% 75% max Genre Female 2994.0 43.757395 21.966561 0.08 27.0 44.0 61.0 82.0 Male 2115.0 42.483385 23.484066 0.08 22.0 46.0 61.0 82.0 Other 1.0 26.000000 NaN 26.00 26.0 26.0 26.0 26.0
/tmp/ipython-input-47-1761252738.py:3: FutureWarning:
Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.
sns.boxplot(x='Genre', y='Age', data=df, palette="Set2")
/usr/local/lib/python3.11/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 128721 (\N{OCTAGONAL SIGN}) missing from font(s) DejaVu Sans.
fig.canvas.print_figure(bytes_io, **kw)
Test t de Student¶
In [ ]:
import scipy.stats as stats
from scipy.stats import mannwhitneyu
# Séparation des groupes
hommes = df[df['Genre'] == 'Homme']['Age']
femmes = df[df['Genre'] == 'Femme']['Age']
t_stat, p_value = stats.ttest_ind(hommes, femmes, equal_var=False)
print(f"T-statistique = {t_stat:.3f}, p-value = {p_value:.4f}")
if p_value < 0.05:
print("L'age diffère significativement selon le genre.")
else:
print("Aucune différence significative de l'age selon le genre.")
T-statistique = nan, p-value = nan Aucune différence significative de l'age selon le genre.
/usr/local/lib/python3.11/dist-packages/scipy/_lib/deprecation.py:234: SmallSampleWarning: One or more sample arguments is too small; all returned values will be NaN. See documentation for sample size requirements. return f(*args, **kwargs)
AVC et Taux_glucose_moyen¶
In [ ]:
Les distribution des groupes ne suivent pas la loi normale utilison les test non paramétrique¶
AVC & Age¶
In [ ]:
sns.histplot(df[df['AVC'] == 0]['Age'], kde=True, color='blue', label='Sans AVC')
sns.histplot(df[df['AVC'] == 1]['Age'], kde=True, color='red', label='Avec AVC')
plt.legend()
plt.title("Histogramme de l'Age par groupe AVC")
plt.show()
# Groupe sans AVC
stat1, p1 = shapiro(df[df['AVC'] == 0]['Age'])
print(f"Sans AVC - Stat={stat1:.3f}, p={p1:.3f}")
# Groupe avec AVC
stat2, p2 = shapiro(df[df['AVC'] == 1]['Age'])
print(f"Avec AVC - Stat={stat2:.3f}, p={p2:.3f}")
Sans AVC - Stat=0.970, p=0.000 Avec AVC - Stat=0.878, p=0.000
In [ ]:
print(df.groupby('AVC')['Age'].describe())
# Boxplot
sns.boxplot(x='AVC', y='Age', data=df, palette="Set2")
plt.title("🛑 Distribution de l'âge selon l'AVC")
plt.xlabel("AVC (0=Non, 1=Oui)")
plt.show()
count mean std min 25% 50% 75% max AVC 0 4861.0 41.971545 22.291940 0.08 24.0 43.0 59.0 82.0 1 249.0 67.728193 12.727419 1.32 59.0 71.0 78.0 82.0
/tmp/ipython-input-50-145994358.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
print(df.groupby('AVC')['Age'].describe())
/tmp/ipython-input-50-145994358.py:3: FutureWarning:
Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.
sns.boxplot(x='AVC', y='Age', data=df, palette="Set2")
/usr/local/lib/python3.11/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 128721 (\N{OCTAGONAL SIGN}) missing from font(s) DejaVu Sans.
fig.canvas.print_figure(bytes_io, **kw)
Test de Man whitney¶
In [ ]:
# Séparation des groupes
age_sans_avc = df[df['AVC'] == 0]['Age']
age_avec_avc = df[df['AVC'] == 1]['Age']
t_stat, p_value = stats.mannwhitneyu(age_sans_avc, age_avec_avc)
print(f"U-statistique = {t_stat:.3f}, p-value = {p_value:.4f}")
if p_value < 0.05:
print("Différence significative de l'âge moyen entre les groupes AVC.")
else:
print("Pas de différence significative de l'âge moyen entre les groupes AVC.")
U-statistique = 200263.500, p-value = 0.0000 Différence significative de l'âge moyen entre les groupes AVC.
AVC & Taux de glycemie¶
In [ ]:
sns.histplot(df[df['AVC'] == 0]['Taux_glucose_moyen'], kde=True, color='blue', label='Sans AVC')
sns.histplot(df[df['AVC'] == 1]['Taux_glucose_moyen'], kde=True, color='red', label='Avec AVC')
plt.legend()
plt.title("Histogramme de la glycémie moyenne par groupe AVC")
plt.show()
# Groupe sans AVC
stat1, p1 = shapiro(df[df['AVC'] == 0]['Taux_glucose_moyen'])
print(f"Sans AVC - Stat={stat1:.3f}, p={p1:.3f}")
# Groupe avec AVC
stat2, p2 = shapiro(df[df['AVC'] == 1]['Taux_glucose_moyen'])
print(f"Avec AVC - Stat={stat2:.3f}, p={p2:.3f}")
Sans AVC - Stat=0.878, p=0.000 Avec AVC - Stat=0.832, p=0.000
In [ ]:
# Moyenne et écart-type de la glycémie selon AVC
print(df.groupby('AVC')['Taux_glucose_moyen'].describe())
sns.boxplot(x='AVC', y='Taux_glucose_moyen', data=df, palette="Set2")
plt.title("🛑 Distribution du taux de glycémie selon l'AVC")
plt.xlabel("AVC (0=Non, 1=Oui)")
plt.show()
count mean std min 25% 50% 75% max AVC 0 4861.0 100.126890 32.484047 55.12 77.12 91.47 112.8300 169.3575 1 249.0 117.967028 41.766465 56.11 79.79 105.22 169.3575 169.3575
/tmp/ipython-input-53-2483663623.py:2: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
print(df.groupby('AVC')['Taux_glucose_moyen'].describe())
/tmp/ipython-input-53-2483663623.py:3: FutureWarning:
Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.
sns.boxplot(x='AVC', y='Taux_glucose_moyen', data=df, palette="Set2")
/usr/local/lib/python3.11/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 128721 (\N{OCTAGONAL SIGN}) missing from font(s) DejaVu Sans.
fig.canvas.print_figure(bytes_io, **kw)
Test de Man Whitney¶
In [ ]:
## séparation des groupes
glicemie_sans_avc = df[df['AVC'] == 0]['Taux_glucose_moyen']
glicemie_avec_avc = df[df['AVC'] == 1]['Taux_glucose_moyen']
t_stat, p_value = stats.mannwhitneyu(glicemie_sans_avc, glicemie_avec_avc)
print(f"Statistique t : {t_stat:.2f}")
print(f"p-value : {p_value:.4f}")
if p_value < 0.05:
print("La différence de glycémie moyenne entre les groupes est significative.")
else:
print("Aucune différence significative de glycémie moyenne entre les groupes.")
Statistique t : 471779.50 p-value : 0.0000 La différence de glycémie moyenne entre les groupes est significative.
AVC & IMC¶
In [ ]:
sns.histplot(df[df['AVC'] == 0]['IMC'], kde=True, color='blue', label='Sans AVC')
sns.histplot(df[df['AVC'] == 1]['IMC'], kde=True, color='red', label='Avec AVC')
plt.legend()
plt.title("Histogramme de l'IMC par groupe AVC")
plt.show()
# Groupe sans AVC
stat1, p1 = shapiro(df[df['AVC'] == 0]['IMC'])
print(f"Sans AVC - Stat={stat1:.3f}, p={p1:.3f}")
# Groupe avec AVC
stat2, p2 = shapiro(df[df['AVC'] == 1]['IMC'])
print(f"Avec AVC - Stat={stat2:.3f}, p={p2:.3f}")
Sans AVC - Stat=0.980, p=0.000 Avec AVC - Stat=0.941, p=0.000
In [ ]:
print(df.groupby('AVC')['IMC'].describe())
sns.boxplot(x='AVC', y='IMC', data=df, palette="Set2")
plt.title("🛑 Distribution de l'IMC selon l'AVC")
plt.xlabel("AVC (0=Non, 1=Oui)")
plt.show()
count mean std min 25% 50% 75% max AVC 0 4861.0 28.647873 7.180836 10.3 23.6 28.300000 32.8 46.3 1 249.0 30.161163 5.625364 16.9 27.0 28.893237 32.5 46.3
/tmp/ipython-input-56-3758547483.py:1: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
print(df.groupby('AVC')['IMC'].describe())
/tmp/ipython-input-56-3758547483.py:2: FutureWarning:
Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.
sns.boxplot(x='AVC', y='IMC', data=df, palette="Set2")
/usr/local/lib/python3.11/dist-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 128721 (\N{OCTAGONAL SIGN}) missing from font(s) DejaVu Sans.
fig.canvas.print_figure(bytes_io, **kw)
Test de Man Whitney¶
In [ ]:
## séparation des groupes
imc_sans_avc = df[df['AVC'] == 0]['IMC']
imc_avec_avc = df[df['AVC'] == 1]['IMC']
t_stat, p_value = stats.mannwhitneyu(imc_sans_avc, imc_avec_avc)
print(f"Statistique t : {t_stat:.2f}")
print(f"p-value : {p_value:.4f}")
if p_value < 0.05:
print("La différence de l'IMC entre les groupes est significative.")
else:
print("Aucune différence significative de l'IMC entre les groupes.")
Statistique t : 515877.00 p-value : 0.0001 La différence de l'IMC entre les groupes est significative.
In [ ]:
In [ ]:
In [ ]:
from scipy.stats import shapiro, levene, ttest_ind, mannwhitneyu
def test_numeriques_vs_avc(df, var_cible='AVC'):
# Sélection des variables numériques sauf la variable cible
numeriques = df.select_dtypes(include=['float64', 'int64']).columns.tolist()
if var_cible in numeriques:
numeriques.remove(var_cible)
print(f"Variables numériques analysées vs {var_cible} : {numeriques}\n")
for var in numeriques:
print(f"--- Analyse de la variable : {var} ---")
# 1. Visualisation boxplot en premier
plt.figure(figsize=(6,4))
sns.boxplot(x=var_cible, y=var, data=df, palette="Set2")
plt.title(f"Distribution de {var} selon {var_cible}")
plt.xlabel(f"{var_cible} (0 = Non, 1 = Oui)")
plt.ylabel(var)
plt.show()
groupe0 = df[df[var_cible] == 0][var].dropna()
groupe1 = df[df[var_cible] == 1][var].dropna()
# Test de normalité
p_norm_0 = shapiro(groupe0).pvalue
p_norm_1 = shapiro(groupe1).pvalue
# Test d'homogénéité des variances
p_levene = levene(groupe0, groupe1).pvalue
print(f"Normalité p-values : groupe 0 = {p_norm_0:.4f}, groupe 1 = {p_norm_1:.4f}")
print(f"Homogénéité des variances (Levene) p-value : {p_levene:.4f}")
# Choix du test
if p_norm_0 > 0.05 and p_norm_1 > 0.05 and p_levene > 0.05:
# Test t de Student
stat, p_val = ttest_ind(groupe0, groupe1, equal_var=True)
test_name = "Test t de Student"
else:
# Test de Mann-Whitney
stat, p_val = mannwhitneyu(groupe0, groupe1)
test_name = "Test de Mann-Whitney"
print(f"{test_name} : stat = {stat:.4f}, p-value = {p_val:.4f}")
if p_val < 0.05:
print(f"--> Différence significative détectée pour la variable {var}\n")
else:
print(f"--> Pas de différence significative pour la variable {var}\n")
#
In [ ]:
test_numeriques_vs_avc(df)
Variables numériques analysées vs AVC : ['Age', 'Taux_glucose_moyen', 'IMC'] --- Analyse de la variable : Age ---
/tmp/ipython-input-58-1001718392.py:16: FutureWarning: Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect. sns.boxplot(x=var_cible, y=var, data=df, palette="Set2")
Normalité p-values : groupe 0 = 0.0000, groupe 1 = 0.0000 Homogénéité des variances (Levene) p-value : 0.0000 Test de Mann-Whitney : stat = 200263.5000, p-value = 0.0000 --> Différence significative détectée pour la variable Age --- Analyse de la variable : Taux_glucose_moyen ---
/tmp/ipython-input-58-1001718392.py:16: FutureWarning: Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect. sns.boxplot(x=var_cible, y=var, data=df, palette="Set2")
Normalité p-values : groupe 0 = 0.0000, groupe 1 = 0.0000 Homogénéité des variances (Levene) p-value : 0.0000 Test de Mann-Whitney : stat = 471779.5000, p-value = 0.0000 --> Différence significative détectée pour la variable Taux_glucose_moyen --- Analyse de la variable : IMC ---
/tmp/ipython-input-58-1001718392.py:16: FutureWarning: Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect. sns.boxplot(x=var_cible, y=var, data=df, palette="Set2")
Normalité p-values : groupe 0 = 0.0000, groupe 1 = 0.0000 Homogénéité des variances (Levene) p-value : 0.0000 Test de Mann-Whitney : stat = 515877.0000, p-value = 0.0001 --> Différence significative détectée pour la variable IMC
Encodage¶
In [ ]:
df.head()
Out[ ]:
| Genre | Age | Hypertension | Maladie_Cardiaque | Situation_Matrimoniale | Type_travail | Residence | Taux_glucose_moyen | IMC | Statut_Fumer | AVC | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Male | 67.0 | 0 | 1 | Yes | Private | Urban | 169.3575 | 36.600000 | formerly smoked | 1 |
| 1 | Female | 61.0 | 0 | 0 | Yes | Self-employed | Rural | 169.3575 | 28.893237 | never smoked | 1 |
| 2 | Male | 80.0 | 0 | 1 | Yes | Private | Rural | 105.9200 | 32.500000 | never smoked | 1 |
| 3 | Female | 49.0 | 0 | 0 | Yes | Private | Urban | 169.3575 | 34.400000 | smokes | 1 |
| 4 | Female | 79.0 | 1 | 0 | Yes | Self-employed | Rural | 169.3575 | 24.000000 | never smoked | 1 |
In [ ]:
label_encoder = LabelEncoder()
df['Genre'] = label_encoder.fit_transform(df['Genre'])
df['Genre'] = df['Genre'].astype('category')
df['Type_travail'] = label_encoder.fit_transform(df['Type_travail'])
df['Type_travail'] = df['Type_travail'].astype('category')
df['Situation_Matrimoniale'] = label_encoder.fit_transform(df['Situation_Matrimoniale'])
df['Situation_Matrimoniale'] = df['Situation_Matrimoniale'].astype('category')
df['Residence'] = label_encoder.fit_transform(df['Residence'])
df['Residence'] = df['Residence'].astype('category')
df['Statut_Fumer'] = label_encoder.fit_transform(df['Statut_Fumer'])
df['Statut_Fumer'] = df['Statut_Fumer'].astype('category')
df.head()
Out[ ]:
| Genre | Age | Hypertension | Maladie_Cardiaque | Situation_Matrimoniale | Type_travail | Residence | Taux_glucose_moyen | IMC | Statut_Fumer | AVC | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 67.0 | 0 | 1 | 1 | 2 | 1 | 169.3575 | 36.600000 | 0 | 1 |
| 1 | 0 | 61.0 | 0 | 0 | 1 | 3 | 0 | 169.3575 | 28.893237 | 1 | 1 |
| 2 | 1 | 80.0 | 0 | 1 | 1 | 2 | 0 | 105.9200 | 32.500000 | 1 | 1 |
| 3 | 0 | 49.0 | 0 | 0 | 1 | 2 | 1 | 169.3575 | 34.400000 | 2 | 1 |
| 4 | 0 | 79.0 | 1 | 0 | 1 | 3 | 0 | 169.3575 | 24.000000 | 1 | 1 |
In [ ]:
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 5110 entries, 0 to 5109 Data columns (total 11 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Genre 5110 non-null category 1 Age 5110 non-null float64 2 Hypertension 5110 non-null category 3 Maladie_Cardiaque 5110 non-null category 4 Situation_Matrimoniale 5110 non-null category 5 Type_travail 5110 non-null category 6 Residence 5110 non-null category 7 Taux_glucose_moyen 5110 non-null float64 8 IMC 5110 non-null float64 9 Statut_Fumer 5110 non-null category 10 AVC 5110 non-null category dtypes: category(8), float64(3) memory usage: 160.9 KB
In [ ]:
df_categorielles = df.select_dtypes(include=['category'])
df_categorielles
Out[ ]:
| Genre | Hypertension | Maladie_Cardiaque | Situation_Matrimoniale | Type_travail | Residence | Statut_Fumer | AVC | |
|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0 | 1 | 1 | 2 | 1 | 0 | 1 |
| 1 | 0 | 0 | 0 | 1 | 3 | 0 | 1 | 1 |
| 2 | 1 | 0 | 1 | 1 | 2 | 0 | 1 | 1 |
| 3 | 0 | 0 | 0 | 1 | 2 | 1 | 2 | 1 |
| 4 | 0 | 1 | 0 | 1 | 3 | 0 | 1 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 5105 | 0 | 1 | 0 | 1 | 2 | 1 | 1 | 0 |
| 5106 | 0 | 0 | 0 | 1 | 3 | 1 | 1 | 0 |
| 5107 | 0 | 0 | 0 | 1 | 3 | 0 | 1 | 0 |
| 5108 | 1 | 0 | 0 | 1 | 2 | 0 | 0 | 0 |
| 5109 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 |
5110 rows × 8 columns
Matrice de corrélation¶
In [ ]:
# Tableau de corrélation
df_numeriques.corr()
# Matrice de corrélation
plt.figure(figsize=(12,8))
sns.heatmap(df.corr() , annot=True, cmap='coolwarm', fmt='.2f', linewidths=0.5)
plt.xticks(rotation=45, ha='right')
Out[ ]:
(array([ 0.5, 1.5, 2.5, 3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5]), [Text(0.5, 0, 'Genre'), Text(1.5, 0, 'Age'), Text(2.5, 0, 'Hypertension'), Text(3.5, 0, 'Maladie_Cardiaque'), Text(4.5, 0, 'Situation_Matrimoniale'), Text(5.5, 0, 'Type_travail'), Text(6.5, 0, 'Residence'), Text(7.5, 0, 'Taux_glucose_moyen'), Text(8.5, 0, 'IMC'), Text(9.5, 0, 'Statut_Fumer'), Text(10.5, 0, 'AVC')])
In [ ]:
correlation_values = abs(df.corr()['AVC'].drop('AVC')).sort_values(ascending=False)
correlation_values
Out[ ]:
| AVC | |
|---|---|
| Age | 0.245257 |
| Maladie_Cardiaque | 0.134914 |
| Hypertension | 0.127904 |
| Taux_glucose_moyen | 0.115652 |
| Situation_Matrimoniale | 0.108340 |
| IMC | 0.045765 |
| Statut_Fumer | 0.037057 |
| Type_travail | 0.032316 |
| Residence | 0.015458 |
| Genre | 0.008929 |
Normalisation des variables numériques¶
In [ ]:
scaler = StandardScaler()
df_numeriques_normalise = pd.DataFrame(scaler.fit_transform(df_numeriques), columns=df_numeriques.columns)
df_numeriques_normalise
Out[ ]:
| Age | Taux_glucose_moyen | IMC | |
|---|---|---|---|
| 0 | 1.051434 | 2.058363 | 1.106633 |
| 1 | 0.786070 | 2.058363 | 0.024107 |
| 2 | 1.626390 | 0.148256 | 0.530729 |
| 3 | 0.255342 | 2.058363 | 0.797611 |
| 4 | 1.582163 | 2.058363 | -0.663218 |
| ... | ... | ... | ... |
| 5105 | 1.626390 | -0.519284 | 0.024107 |
| 5106 | 1.670617 | 0.728778 | 1.584211 |
| 5107 | -0.363842 | -0.542168 | 0.263846 |
| 5108 | 0.343796 | 1.966000 | -0.438475 |
| 5109 | 0.034205 | -0.473216 | -0.354197 |
5110 rows × 3 columns
Concaténation des deux bases (categorielle et numerique)¶
In [ ]:
df_final = pd.concat([df_numeriques_normalise,df_categorielles], axis=1)
df_final
Out[ ]:
| Age | Taux_glucose_moyen | IMC | Genre | Hypertension | Maladie_Cardiaque | Situation_Matrimoniale | Type_travail | Residence | Statut_Fumer | AVC | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1.051434 | 2.058363 | 1.106633 | 1 | 0 | 1 | 1 | 2 | 1 | 0 | 1 |
| 1 | 0.786070 | 2.058363 | 0.024107 | 0 | 0 | 0 | 1 | 3 | 0 | 1 | 1 |
| 2 | 1.626390 | 0.148256 | 0.530729 | 1 | 0 | 1 | 1 | 2 | 0 | 1 | 1 |
| 3 | 0.255342 | 2.058363 | 0.797611 | 0 | 0 | 0 | 1 | 2 | 1 | 2 | 1 |
| 4 | 1.582163 | 2.058363 | -0.663218 | 0 | 1 | 0 | 1 | 3 | 0 | 1 | 1 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 5105 | 1.626390 | -0.519284 | 0.024107 | 0 | 1 | 0 | 1 | 2 | 1 | 1 | 0 |
| 5106 | 1.670617 | 0.728778 | 1.584211 | 0 | 0 | 0 | 1 | 3 | 1 | 1 | 0 |
| 5107 | -0.363842 | -0.542168 | 0.263846 | 0 | 0 | 0 | 1 | 3 | 0 | 1 | 0 |
| 5108 | 0.343796 | 1.966000 | -0.438475 | 1 | 0 | 0 | 1 | 2 | 0 | 0 | 0 |
| 5109 | 0.034205 | -0.473216 | -0.354197 | 0 | 0 | 0 | 1 | 0 | 1 | 1 | 0 |
5110 rows × 11 columns
In [ ]:
df_final.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 5110 entries, 0 to 5109 Data columns (total 11 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Age 5110 non-null float64 1 Taux_glucose_moyen 5110 non-null float64 2 IMC 5110 non-null float64 3 Genre 5110 non-null category 4 Hypertension 5110 non-null category 5 Maladie_Cardiaque 5110 non-null category 6 Situation_Matrimoniale 5110 non-null category 7 Type_travail 5110 non-null category 8 Residence 5110 non-null category 9 Statut_Fumer 5110 non-null category 10 AVC 5110 non-null category dtypes: category(8), float64(3) memory usage: 160.9 KB
In [ ]:
df['Hypertension'] = df['Hypertension'].astype('int')
df['Maladie_Cardiaque'] = df['Maladie_Cardiaque'].astype('int')
df['Genre'] = df['Genre'].astype('int')
df['Type_travail'] = df['Type_travail'].astype('int')
df['Situation_Matrimoniale'] = df['Situation_Matrimoniale'].astype('int')
df['Residence'] = df['Residence'].astype('int')
df['Statut_Fumer'] = df['Statut_Fumer'].astype('int')
df['AVC'] = df['AVC'].astype('int')
df.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 5110 entries, 0 to 5109 Data columns (total 11 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Genre 5110 non-null int64 1 Age 5110 non-null float64 2 Hypertension 5110 non-null int64 3 Maladie_Cardiaque 5110 non-null int64 4 Situation_Matrimoniale 5110 non-null int64 5 Type_travail 5110 non-null int64 6 Residence 5110 non-null int64 7 Taux_glucose_moyen 5110 non-null float64 8 IMC 5110 non-null float64 9 Statut_Fumer 5110 non-null int64 10 AVC 5110 non-null int64 dtypes: float64(3), int64(8) memory usage: 439.3 KB
Modélisation¶
Séparation des données¶
In [ ]:
X = df_final.drop(['AVC','Residence'], axis = 1)
Y = df_final['AVC']
In [ ]:
X['Genre'] = X['Genre'].astype('int')
X['Maladie_Cardiaque'] = X['Maladie_Cardiaque'].astype('int')
X['Hypertension'] = X['Hypertension'].astype('int')
X['Type_travail'] = X['Type_travail'].astype('int')
X['Situation_Matrimoniale'] = X['Situation_Matrimoniale'].astype('int')
#X['Residence'] = X['Residence'].astype('int')
X['Statut_Fumer'] = X['Statut_Fumer'].astype('int')
print(X.dtypes)
Age float64 Taux_glucose_moyen float64 IMC float64 Genre int64 Hypertension int64 Maladie_Cardiaque int64 Situation_Matrimoniale int64 Type_travail int64 Statut_Fumer int64 dtype: object
Oversampling avec SMOTE¶
In [ ]:
from imblearn.over_sampling import SMOTE
smote = SMOTE(k_neighbors=5, random_state=42)
X_equi, Y_equi = smote.fit_resample(X, Y)
print("dimension de X: ", X_equi.shape)
print("dimension de Y: ", Y_equi.shape)
dimension de X: (9722, 9) dimension de Y: (9722,)
In [ ]:
plt.subplot(1, 2, 2)
plt.bar([0, 1], [np.sum(Y_equi == 0), np.sum(Y_equi == 1)], edgecolor='black')
plt.title("Distribution des classes après SMOTE")
plt.xlabel("Classe")
plt.ylabel("Fréquence")
plt.xticks([0, 1], ['oui', 'Non'])
Out[ ]:
([<matplotlib.axis.XTick at 0x7f26f77322d0>, <matplotlib.axis.XTick at 0x7f26fa5bed50>], [Text(0, 0, 'oui'), Text(1, 0, 'Non')])
In [ ]:
# Division de la base de données en train et test
X_train,X_test,Y_train,Y_test = train_test_split(X_equi, Y_equi, test_size=0.3, random_state=2)
In [ ]:
print(X_train.shape)
print(X_test.shape)
print(Y_train.shape)
print(Y_test.shape)
(6805, 9) (2917, 9) (6805,) (2917,)
Apprentissage et application du modèle¶
Regression logistique¶
In [ ]:
modele_RL = LogisticRegression(max_iter=1000)
Entrainement du modèle¶
In [ ]:
modele_RL.fit(X_train,Y_train)
Out[ ]:
LogisticRegression(max_iter=1000)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
LogisticRegression(max_iter=1000)
Prédiction¶
In [ ]:
# Prédiction sur la base test
best_Y_test_pred_RL = modele_RL.predict(X_test)
best_Y_test_pred_RL
Out[ ]:
array([1, 0, 0, ..., 0, 0, 0])
In [ ]:
# Prédiction sur la base train
best_Y_train_pred_RL = modele_RL.predict(X_train)
best_Y_train_pred_RL
Out[ ]:
array([1, 0, 1, ..., 1, 0, 1])
In [ ]:
# prediction en proba sur la base train
Y_pred_train_proba_RL = modele_RL.predict_proba(X_train)
Y_pred_train_proba_RL
Out[ ]:
array([[0.20608268, 0.79391732],
[0.63717569, 0.36282431],
[0.20006969, 0.79993031],
...,
[0.1952449 , 0.8047551 ],
[0.93209852, 0.06790148],
[0.42528603, 0.57471397]])
Mesure de performance¶
Accuracy¶
In [ ]:
# Sur la base test
Acc_test_RL = accuracy_score(Y_test, best_Y_test_pred_RL) * 100
Acc_train_RL = accuracy_score(Y_train, best_Y_train_pred_RL) * 100
# Sur la base train
print("Accuracy sur train:", Acc_train_RL, "%")
print("Accuracy sur test:", Acc_test_RL, "%")
Accuracy sur train: 78.14842027920646 % Accuracy sur test: 78.02536852931094 %
F1 score¶
In [ ]:
# Sur la base test et train
F1_score_test_RL = f1_score(Y_test, best_Y_test_pred_RL, average='weighted') * 100
F1_score_train_RL = f1_score(Y_train, best_Y_train_pred_RL, average='weighted') * 100
# Sur la base train
print("F1_score sur train:", F1_score_train_RL, "%")
print("F1_score sur test:", F1_score_test_RL, "%")
F1_score sur train: 78.1158804890842 % F1_score sur test: 77.99992059321391 %
Précision¶
In [ ]:
# Sur la base test et train
precision_score_test_RL = precision_score(Y_test, best_Y_test_pred_RL, average='weighted') * 100
precision_score_train_RL = precision_score(Y_train, best_Y_train_pred_RL, average='weighted') * 100
# Sur la base train
print("Précision sur train:", precision_score_train_RL, "%")
print("Précision sur test:", precision_score_test_RL, "%")
Précision sur train: 78.30383775754254 % Précision sur test: 78.18523891560424 %
Recall¶
In [ ]:
# Sur la base test et train
recall_score_test_RL = recall_score(Y_test, best_Y_test_pred_RL, average='weighted') * 100
recall_score_train_RL = recall_score(Y_train, best_Y_train_pred_RL, average='weighted') * 100
# Sur la base train
print("Recall sur train:", recall_score_train_RL, "%")
print("Recall sur test:", recall_score_test_RL, "%")
Recall sur train: 78.14842027920646 % Recall sur test: 78.02536852931094 %
Matrice de confusion¶
In [ ]:
matrice_RL = confusion_matrix(Y_test, best_Y_test_pred_RL)
In [ ]:
from mlxtend.plotting import plot_confusion_matrix
# Affichage de la matrice de confusion avec titre
fig, ax = plot_confusion_matrix(conf_mat=matrice_RL,
show_absolute=True,
show_normed=True,
colorbar=True)
plt.title("Confusion Matrix RL")
plt.xlabel('Prédit')
plt.ylabel('Réel')
plt.show()
print("Alors dans la classe 0,sur",Y_test.value_counts()[0],
"individu,le modèle réussit à faire un bon classement sur", matrice_RL[0,0],
" individu et une erreur sur", matrice_RL[0,1], "\n Dans la classe 1,sur",Y_test.value_counts()[1],
"individus le modèle fait un bon classement sur", matrice_RL[1,1],
" individu et une erreur sur", matrice_RL[0,0] )
Alors dans la classe 0,sur 1466 individu,le modèle réussit à faire un bon classement sur 1092 individu et une erreur sur 374 Dans la classe 1,sur 1451 individus le modèle fait un bon classement sur 1184 individu et une erreur sur 1092
*# taux de bon et mauvais classement*
In [ ]:
print(f"Taux de bon classement (Classe 0 - Sans AVC): {matrice_RL[0, 0] / Y_test.value_counts()[0] * 100:.2f} %")
print(f"Taux de mauvais classement (Classe 0 - Sans AVC): {matrice_RL[0, 1] / Y_test.value_counts()[0] * 100:.2f} %")
print(f"Taux de bon classement (Classe 1 - Avec AVC): {matrice_RL[1, 1] / Y_test.value_counts()[1] * 100:.2f} %")
print(f"Taux de mauvais classement (Classe 1 - Avec AVC): {matrice_RL[1, 0] / Y_test.value_counts()[1] * 100:.2f} %")
Taux de bon classement (Classe 0 - Sans AVC): 74.49 % Taux de mauvais classement (Classe 0 - Sans AVC): 25.51 % Taux de bon classement (Classe 1 - Avec AVC): 81.60 % Taux de mauvais classement (Classe 1 - Avec AVC): 18.40 %
In [ ]:
# Courbe ROC
fpr_RL, tpr_RL, thresholds_RL = roc_curve(Y_test, modele_RL.predict_proba(X_test)[:, 1])
roc_auc_RL = auc(fpr_RL, tpr_RL)
plt.figure()
plt.plot(fpr_RL, tpr_RL, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc_RL)
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('Taux de faux positif')
plt.ylabel('Taux de vrai positif')
plt.title('Courbe de ROC pour Logistic Regression')
plt.legend(loc="lower right")
plt.show()
In [ ]:
from sklearn.inspection import permutation_importance
result = permutation_importance(modele_RL, X, Y, n_repeats=10)
sorted_idx = result.importances_mean.argsort()
plt.barh(X.columns[sorted_idx], result.importances_mean[sorted_idx])
plt.xlabel("Importance des caractéristiques")
plt.show()
Arbre de décision¶
Optimisation des parametres avec GridSearchCV¶
In [ ]:
# Définir le modèle et les paramètres
modele_AD = DecisionTreeClassifier()
param_grid = [{
'max_features':[1,2,3,4,5,6,7],
'max_depth':[1,2,3,4,5,6,7,8,9,10]
}
]
# Créer l'objet GridSearchCV
modele_opt_AD = GridSearchCV(modele_AD, # modèle initialisé
param_grid, # grilles de parametre du modèle
cv=5, # cross--validation
verbose=15 #longueur
)
# Entrainement du modèle
modele_opt_AD.fit(X_train,Y_train)
# parametre optimaux
best_param = modele_opt_AD.best_params_
print("VOILA LES MEILLEURS PARAMÉTRES :",best_param)
# meilleur modèle
best_modele_AD = modele_opt_AD.best_estimator_
best_modele_AD
Fitting 5 folds for each of 70 candidates, totalling 350 fits
[CV 1/5; 1/70] START max_depth=1, max_features=1................................
[CV 1/5; 1/70] END .max_depth=1, max_features=1;, score=0.584 total time= 0.0s
[CV 2/5; 1/70] START max_depth=1, max_features=1................................
[CV 2/5; 1/70] END .max_depth=1, max_features=1;, score=0.522 total time= 0.0s
[CV 3/5; 1/70] START max_depth=1, max_features=1................................
[CV 3/5; 1/70] END .max_depth=1, max_features=1;, score=0.527 total time= 0.0s
[CV 4/5; 1/70] START max_depth=1, max_features=1................................
[CV 4/5; 1/70] END .max_depth=1, max_features=1;, score=0.573 total time= 0.0s
[CV 5/5; 1/70] START max_depth=1, max_features=1................................
[CV 5/5; 1/70] END .max_depth=1, max_features=1;, score=0.511 total time= 0.0s
[CV 1/5; 2/70] START max_depth=1, max_features=2................................
[CV 1/5; 2/70] END .max_depth=1, max_features=2;, score=0.582 total time= 0.0s
[CV 2/5; 2/70] START max_depth=1, max_features=2................................
[CV 2/5; 2/70] END .max_depth=1, max_features=2;, score=0.788 total time= 0.0s
[CV 3/5; 2/70] START max_depth=1, max_features=2................................
[CV 3/5; 2/70] END .max_depth=1, max_features=2;, score=0.565 total time= 0.0s
[CV 4/5; 2/70] START max_depth=1, max_features=2................................
[CV 4/5; 2/70] END .max_depth=1, max_features=2;, score=0.573 total time= 0.0s
[CV 5/5; 2/70] START max_depth=1, max_features=2................................
[CV 5/5; 2/70] END .max_depth=1, max_features=2;, score=0.597 total time= 0.0s
[CV 1/5; 3/70] START max_depth=1, max_features=3................................
[CV 1/5; 3/70] END .max_depth=1, max_features=3;, score=0.572 total time= 0.0s
[CV 2/5; 3/70] START max_depth=1, max_features=3................................
[CV 2/5; 3/70] END .max_depth=1, max_features=3;, score=0.583 total time= 0.0s
[CV 3/5; 3/70] START max_depth=1, max_features=3................................
[CV 3/5; 3/70] END .max_depth=1, max_features=3;, score=0.602 total time= 0.0s
[CV 4/5; 3/70] START max_depth=1, max_features=3................................
[CV 4/5; 3/70] END .max_depth=1, max_features=3;, score=0.583 total time= 0.0s
[CV 5/5; 3/70] START max_depth=1, max_features=3................................
[CV 5/5; 3/70] END .max_depth=1, max_features=3;, score=0.597 total time= 0.0s
[CV 1/5; 4/70] START max_depth=1, max_features=4................................
[CV 1/5; 4/70] END .max_depth=1, max_features=4;, score=0.592 total time= 0.0s
[CV 2/5; 4/70] START max_depth=1, max_features=4................................
[CV 2/5; 4/70] END .max_depth=1, max_features=4;, score=0.788 total time= 0.0s
[CV 3/5; 4/70] START max_depth=1, max_features=4................................
[CV 3/5; 4/70] END .max_depth=1, max_features=4;, score=0.780 total time= 0.0s
[CV 4/5; 4/70] START max_depth=1, max_features=4................................
[CV 4/5; 4/70] END .max_depth=1, max_features=4;, score=0.573 total time= 0.0s
[CV 5/5; 4/70] START max_depth=1, max_features=4................................
[CV 5/5; 4/70] END .max_depth=1, max_features=4;, score=0.769 total time= 0.0s
[CV 1/5; 5/70] START max_depth=1, max_features=5................................
[CV 1/5; 5/70] END .max_depth=1, max_features=5;, score=0.608 total time= 0.0s
[CV 2/5; 5/70] START max_depth=1, max_features=5................................
[CV 2/5; 5/70] END .max_depth=1, max_features=5;, score=0.572 total time= 0.0s
[CV 3/5; 5/70] START max_depth=1, max_features=5................................
[CV 3/5; 5/70] END .max_depth=1, max_features=5;, score=0.565 total time= 0.0s
[CV 4/5; 5/70] START max_depth=1, max_features=5................................
[CV 4/5; 5/70] END .max_depth=1, max_features=5;, score=0.790 total time= 0.0s
[CV 5/5; 5/70] START max_depth=1, max_features=5................................
[CV 5/5; 5/70] END .max_depth=1, max_features=5;, score=0.557 total time= 0.0s
[CV 1/5; 6/70] START max_depth=1, max_features=6................................
[CV 1/5; 6/70] END .max_depth=1, max_features=6;, score=0.592 total time= 0.0s
[CV 2/5; 6/70] START max_depth=1, max_features=6................................
[CV 2/5; 6/70] END .max_depth=1, max_features=6;, score=0.788 total time= 0.0s
[CV 3/5; 6/70] START max_depth=1, max_features=6................................
[CV 3/5; 6/70] END .max_depth=1, max_features=6;, score=0.780 total time= 0.0s
[CV 4/5; 6/70] START max_depth=1, max_features=6................................
[CV 4/5; 6/70] END .max_depth=1, max_features=6;, score=0.623 total time= 0.0s
[CV 5/5; 6/70] START max_depth=1, max_features=6................................
[CV 5/5; 6/70] END .max_depth=1, max_features=6;, score=0.557 total time= 0.0s
[CV 1/5; 7/70] START max_depth=1, max_features=7................................
[CV 1/5; 7/70] END .max_depth=1, max_features=7;, score=0.796 total time= 0.0s
[CV 2/5; 7/70] START max_depth=1, max_features=7................................
[CV 2/5; 7/70] END .max_depth=1, max_features=7;, score=0.572 total time= 0.0s
[CV 3/5; 7/70] START max_depth=1, max_features=7................................
[CV 3/5; 7/70] END .max_depth=1, max_features=7;, score=0.780 total time= 0.0s
[CV 4/5; 7/70] START max_depth=1, max_features=7................................
[CV 4/5; 7/70] END .max_depth=1, max_features=7;, score=0.790 total time= 0.0s
[CV 5/5; 7/70] START max_depth=1, max_features=7................................
[CV 5/5; 7/70] END .max_depth=1, max_features=7;, score=0.769 total time= 0.0s
[CV 1/5; 8/70] START max_depth=2, max_features=1................................
[CV 1/5; 8/70] END .max_depth=2, max_features=1;, score=0.563 total time= 0.0s
[CV 2/5; 8/70] START max_depth=2, max_features=1................................
[CV 2/5; 8/70] END .max_depth=2, max_features=1;, score=0.572 total time= 0.0s
[CV 3/5; 8/70] START max_depth=2, max_features=1................................
[CV 3/5; 8/70] END .max_depth=2, max_features=1;, score=0.616 total time= 0.0s
[CV 4/5; 8/70] START max_depth=2, max_features=1................................
[CV 4/5; 8/70] END .max_depth=2, max_features=1;, score=0.571 total time= 0.0s
[CV 5/5; 8/70] START max_depth=2, max_features=1................................
[CV 5/5; 8/70] END .max_depth=2, max_features=1;, score=0.586 total time= 0.0s
[CV 1/5; 9/70] START max_depth=2, max_features=2................................
[CV 1/5; 9/70] END .max_depth=2, max_features=2;, score=0.796 total time= 0.0s
[CV 2/5; 9/70] START max_depth=2, max_features=2................................
[CV 2/5; 9/70] END .max_depth=2, max_features=2;, score=0.664 total time= 0.0s
[CV 3/5; 9/70] START max_depth=2, max_features=2................................
[CV 3/5; 9/70] END .max_depth=2, max_features=2;, score=0.619 total time= 0.0s
[CV 4/5; 9/70] START max_depth=2, max_features=2................................
[CV 4/5; 9/70] END .max_depth=2, max_features=2;, score=0.790 total time= 0.0s
[CV 5/5; 9/70] START max_depth=2, max_features=2................................
[CV 5/5; 9/70] END .max_depth=2, max_features=2;, score=0.613 total time= 0.0s
[CV 1/5; 10/70] START max_depth=2, max_features=3...............................
[CV 1/5; 10/70] END max_depth=2, max_features=3;, score=0.796 total time= 0.0s
[CV 2/5; 10/70] START max_depth=2, max_features=3...............................
[CV 2/5; 10/70] END max_depth=2, max_features=3;, score=0.609 total time= 0.0s
[CV 3/5; 10/70] START max_depth=2, max_features=3...............................
[CV 3/5; 10/70] END max_depth=2, max_features=3;, score=0.780 total time= 0.0s
[CV 4/5; 10/70] START max_depth=2, max_features=3...............................
[CV 4/5; 10/70] END max_depth=2, max_features=3;, score=0.583 total time= 0.0s
[CV 5/5; 10/70] START max_depth=2, max_features=3...............................
[CV 5/5; 10/70] END max_depth=2, max_features=3;, score=0.623 total time= 0.0s
[CV 1/5; 11/70] START max_depth=2, max_features=4...............................
[CV 1/5; 11/70] END max_depth=2, max_features=4;, score=0.598 total time= 0.0s
[CV 2/5; 11/70] START max_depth=2, max_features=4...............................
[CV 2/5; 11/70] END max_depth=2, max_features=4;, score=0.791 total time= 0.0s
[CV 3/5; 11/70] START max_depth=2, max_features=4...............................
[CV 3/5; 11/70] END max_depth=2, max_features=4;, score=0.616 total time= 0.0s
[CV 4/5; 11/70] START max_depth=2, max_features=4...............................
[CV 4/5; 11/70] END max_depth=2, max_features=4;, score=0.790 total time= 0.0s
[CV 5/5; 11/70] START max_depth=2, max_features=4...............................
[CV 5/5; 11/70] END max_depth=2, max_features=4;, score=0.769 total time= 0.0s
[CV 1/5; 12/70] START max_depth=2, max_features=5...............................
[CV 1/5; 12/70] END max_depth=2, max_features=5;, score=0.760 total time= 0.0s
[CV 2/5; 12/70] START max_depth=2, max_features=5...............................
[CV 2/5; 12/70] END max_depth=2, max_features=5;, score=0.788 total time= 0.0s
[CV 3/5; 12/70] START max_depth=2, max_features=5...............................
[CV 3/5; 12/70] END max_depth=2, max_features=5;, score=0.750 total time= 0.0s
[CV 4/5; 12/70] START max_depth=2, max_features=5...............................
[CV 4/5; 12/70] END max_depth=2, max_features=5;, score=0.790 total time= 0.0s
[CV 5/5; 12/70] START max_depth=2, max_features=5...............................
[CV 5/5; 12/70] END max_depth=2, max_features=5;, score=0.769 total time= 0.0s
[CV 1/5; 13/70] START max_depth=2, max_features=6...............................
[CV 1/5; 13/70] END max_depth=2, max_features=6;, score=0.796 total time= 0.0s
[CV 2/5; 13/70] START max_depth=2, max_features=6...............................
[CV 2/5; 13/70] END max_depth=2, max_features=6;, score=0.788 total time= 0.0s
[CV 3/5; 13/70] START max_depth=2, max_features=6...............................
[CV 3/5; 13/70] END max_depth=2, max_features=6;, score=0.780 total time= 0.0s
[CV 4/5; 13/70] START max_depth=2, max_features=6...............................
[CV 4/5; 13/70] END max_depth=2, max_features=6;, score=0.790 total time= 0.0s
[CV 5/5; 13/70] START max_depth=2, max_features=6...............................
[CV 5/5; 13/70] END max_depth=2, max_features=6;, score=0.769 total time= 0.0s
[CV 1/5; 14/70] START max_depth=2, max_features=7...............................
[CV 1/5; 14/70] END max_depth=2, max_features=7;, score=0.796 total time= 0.0s
[CV 2/5; 14/70] START max_depth=2, max_features=7...............................
[CV 2/5; 14/70] END max_depth=2, max_features=7;, score=0.788 total time= 0.0s
[CV 3/5; 14/70] START max_depth=2, max_features=7...............................
[CV 3/5; 14/70] END max_depth=2, max_features=7;, score=0.773 total time= 0.0s
[CV 4/5; 14/70] START max_depth=2, max_features=7...............................
[CV 4/5; 14/70] END max_depth=2, max_features=7;, score=0.790 total time= 0.0s
[CV 5/5; 14/70] START max_depth=2, max_features=7...............................
[CV 5/5; 14/70] END max_depth=2, max_features=7;, score=0.769 total time= 0.0s
[CV 1/5; 15/70] START max_depth=3, max_features=1...............................
[CV 1/5; 15/70] END max_depth=3, max_features=1;, score=0.673 total time= 0.0s
[CV 2/5; 15/70] START max_depth=3, max_features=1...............................
[CV 2/5; 15/70] END max_depth=3, max_features=1;, score=0.788 total time= 0.0s
[CV 3/5; 15/70] START max_depth=3, max_features=1...............................
[CV 3/5; 15/70] END max_depth=3, max_features=1;, score=0.586 total time= 0.0s
[CV 4/5; 15/70] START max_depth=3, max_features=1...............................
[CV 4/5; 15/70] END max_depth=3, max_features=1;, score=0.598 total time= 0.0s
[CV 5/5; 15/70] START max_depth=3, max_features=1...............................
[CV 5/5; 15/70] END max_depth=3, max_features=1;, score=0.600 total time= 0.0s
[CV 1/5; 16/70] START max_depth=3, max_features=2...............................
[CV 1/5; 16/70] END max_depth=3, max_features=2;, score=0.796 total time= 0.0s
[CV 2/5; 16/70] START max_depth=3, max_features=2...............................
[CV 2/5; 16/70] END max_depth=3, max_features=2;, score=0.602 total time= 0.0s
[CV 3/5; 16/70] START max_depth=3, max_features=2...............................
[CV 3/5; 16/70] END max_depth=3, max_features=2;, score=0.617 total time= 0.0s
[CV 4/5; 16/70] START max_depth=3, max_features=2...............................
[CV 4/5; 16/70] END max_depth=3, max_features=2;, score=0.664 total time= 0.0s
[CV 5/5; 16/70] START max_depth=3, max_features=2...............................
[CV 5/5; 16/70] END max_depth=3, max_features=2;, score=0.728 total time= 0.0s
[CV 1/5; 17/70] START max_depth=3, max_features=3...............................
[CV 1/5; 17/70] END max_depth=3, max_features=3;, score=0.797 total time= 0.0s
[CV 2/5; 17/70] START max_depth=3, max_features=3...............................
[CV 2/5; 17/70] END max_depth=3, max_features=3;, score=0.743 total time= 0.0s
[CV 3/5; 17/70] START max_depth=3, max_features=3...............................
[CV 3/5; 17/70] END max_depth=3, max_features=3;, score=0.783 total time= 0.0s
[CV 4/5; 17/70] START max_depth=3, max_features=3...............................
[CV 4/5; 17/70] END max_depth=3, max_features=3;, score=0.792 total time= 0.0s
[CV 5/5; 17/70] START max_depth=3, max_features=3...............................
[CV 5/5; 17/70] END max_depth=3, max_features=3;, score=0.772 total time= 0.0s
[CV 1/5; 18/70] START max_depth=3, max_features=4...............................
[CV 1/5; 18/70] END max_depth=3, max_features=4;, score=0.802 total time= 0.0s
[CV 2/5; 18/70] START max_depth=3, max_features=4...............................
[CV 2/5; 18/70] END max_depth=3, max_features=4;, score=0.611 total time= 0.0s
[CV 3/5; 18/70] START max_depth=3, max_features=4...............................
[CV 3/5; 18/70] END max_depth=3, max_features=4;, score=0.780 total time= 0.0s
[CV 4/5; 18/70] START max_depth=3, max_features=4...............................
[CV 4/5; 18/70] END max_depth=3, max_features=4;, score=0.790 total time= 0.0s
[CV 5/5; 18/70] START max_depth=3, max_features=4...............................
[CV 5/5; 18/70] END max_depth=3, max_features=4;, score=0.771 total time= 0.0s
[CV 1/5; 19/70] START max_depth=3, max_features=5...............................
[CV 1/5; 19/70] END max_depth=3, max_features=5;, score=0.750 total time= 0.0s
[CV 2/5; 19/70] START max_depth=3, max_features=5...............................
[CV 2/5; 19/70] END max_depth=3, max_features=5;, score=0.788 total time= 0.0s
[CV 3/5; 19/70] START max_depth=3, max_features=5...............................
[CV 3/5; 19/70] END max_depth=3, max_features=5;, score=0.780 total time= 0.0s
[CV 4/5; 19/70] START max_depth=3, max_features=5...............................
[CV 4/5; 19/70] END max_depth=3, max_features=5;, score=0.789 total time= 0.0s
[CV 5/5; 19/70] START max_depth=3, max_features=5...............................
[CV 5/5; 19/70] END max_depth=3, max_features=5;, score=0.774 total time= 0.0s
[CV 1/5; 20/70] START max_depth=3, max_features=6...............................
[CV 1/5; 20/70] END max_depth=3, max_features=6;, score=0.796 total time= 0.0s
[CV 2/5; 20/70] START max_depth=3, max_features=6...............................
[CV 2/5; 20/70] END max_depth=3, max_features=6;, score=0.788 total time= 0.0s
[CV 3/5; 20/70] START max_depth=3, max_features=6...............................
[CV 3/5; 20/70] END max_depth=3, max_features=6;, score=0.780 total time= 0.0s
[CV 4/5; 20/70] START max_depth=3, max_features=6...............................
[CV 4/5; 20/70] END max_depth=3, max_features=6;, score=0.764 total time= 0.0s
[CV 5/5; 20/70] START max_depth=3, max_features=6...............................
[CV 5/5; 20/70] END max_depth=3, max_features=6;, score=0.774 total time= 0.0s
[CV 1/5; 21/70] START max_depth=3, max_features=7...............................
[CV 1/5; 21/70] END max_depth=3, max_features=7;, score=0.773 total time= 0.0s
[CV 2/5; 21/70] START max_depth=3, max_features=7...............................
[CV 2/5; 21/70] END max_depth=3, max_features=7;, score=0.794 total time= 0.0s
[CV 3/5; 21/70] START max_depth=3, max_features=7...............................
[CV 3/5; 21/70] END max_depth=3, max_features=7;, score=0.773 total time= 0.0s
[CV 4/5; 21/70] START max_depth=3, max_features=7...............................
[CV 4/5; 21/70] END max_depth=3, max_features=7;, score=0.796 total time= 0.0s
[CV 5/5; 21/70] START max_depth=3, max_features=7...............................
[CV 5/5; 21/70] END max_depth=3, max_features=7;, score=0.769 total time= 0.0s
[CV 1/5; 22/70] START max_depth=4, max_features=1...............................
[CV 1/5; 22/70] END max_depth=4, max_features=1;, score=0.688 total time= 0.0s
[CV 2/5; 22/70] START max_depth=4, max_features=1...............................
[CV 2/5; 22/70] END max_depth=4, max_features=1;, score=0.788 total time= 0.0s
[CV 3/5; 22/70] START max_depth=4, max_features=1...............................
[CV 3/5; 22/70] END max_depth=4, max_features=1;, score=0.774 total time= 0.0s
[CV 4/5; 22/70] START max_depth=4, max_features=1...............................
[CV 4/5; 22/70] END max_depth=4, max_features=1;, score=0.675 total time= 0.0s
[CV 5/5; 22/70] START max_depth=4, max_features=1...............................
[CV 5/5; 22/70] END max_depth=4, max_features=1;, score=0.730 total time= 0.0s
[CV 1/5; 23/70] START max_depth=4, max_features=2...............................
[CV 1/5; 23/70] END max_depth=4, max_features=2;, score=0.760 total time= 0.0s
[CV 2/5; 23/70] START max_depth=4, max_features=2...............................
[CV 2/5; 23/70] END max_depth=4, max_features=2;, score=0.641 total time= 0.0s
[CV 3/5; 23/70] START max_depth=4, max_features=2...............................
[CV 3/5; 23/70] END max_depth=4, max_features=2;, score=0.759 total time= 0.0s
[CV 4/5; 23/70] START max_depth=4, max_features=2...............................
[CV 4/5; 23/70] END max_depth=4, max_features=2;, score=0.792 total time= 0.0s
[CV 5/5; 23/70] START max_depth=4, max_features=2...............................
[CV 5/5; 23/70] END max_depth=4, max_features=2;, score=0.692 total time= 0.0s
[CV 1/5; 24/70] START max_depth=4, max_features=3...............................
[CV 1/5; 24/70] END max_depth=4, max_features=3;, score=0.803 total time= 0.0s
[CV 2/5; 24/70] START max_depth=4, max_features=3...............................
[CV 2/5; 24/70] END max_depth=4, max_features=3;, score=0.773 total time= 0.0s
[CV 3/5; 24/70] START max_depth=4, max_features=3...............................
[CV 3/5; 24/70] END max_depth=4, max_features=3;, score=0.693 total time= 0.0s
[CV 4/5; 24/70] START max_depth=4, max_features=3...............................
[CV 4/5; 24/70] END max_depth=4, max_features=3;, score=0.788 total time= 0.0s
[CV 5/5; 24/70] START max_depth=4, max_features=3...............................
[CV 5/5; 24/70] END max_depth=4, max_features=3;, score=0.770 total time= 0.0s
[CV 1/5; 25/70] START max_depth=4, max_features=4...............................
[CV 1/5; 25/70] END max_depth=4, max_features=4;, score=0.796 total time= 0.0s
[CV 2/5; 25/70] START max_depth=4, max_features=4...............................
[CV 2/5; 25/70] END max_depth=4, max_features=4;, score=0.790 total time= 0.0s
[CV 3/5; 25/70] START max_depth=4, max_features=4...............................
[CV 3/5; 25/70] END max_depth=4, max_features=4;, score=0.788 total time= 0.0s
[CV 4/5; 25/70] START max_depth=4, max_features=4...............................
[CV 4/5; 25/70] END max_depth=4, max_features=4;, score=0.743 total time= 0.0s
[CV 5/5; 25/70] START max_depth=4, max_features=4...............................
[CV 5/5; 25/70] END max_depth=4, max_features=4;, score=0.735 total time= 0.0s
[CV 1/5; 26/70] START max_depth=4, max_features=5...............................
[CV 1/5; 26/70] END max_depth=4, max_features=5;, score=0.794 total time= 0.0s
[CV 2/5; 26/70] START max_depth=4, max_features=5...............................
[CV 2/5; 26/70] END max_depth=4, max_features=5;, score=0.801 total time= 0.0s
[CV 3/5; 26/70] START max_depth=4, max_features=5...............................
[CV 3/5; 26/70] END max_depth=4, max_features=5;, score=0.788 total time= 0.0s
[CV 4/5; 26/70] START max_depth=4, max_features=5...............................
[CV 4/5; 26/70] END max_depth=4, max_features=5;, score=0.773 total time= 0.0s
[CV 5/5; 26/70] START max_depth=4, max_features=5...............................
[CV 5/5; 26/70] END max_depth=4, max_features=5;, score=0.769 total time= 0.0s
[CV 1/5; 27/70] START max_depth=4, max_features=6...............................
[CV 1/5; 27/70] END max_depth=4, max_features=6;, score=0.804 total time= 0.0s
[CV 2/5; 27/70] START max_depth=4, max_features=6...............................
[CV 2/5; 27/70] END max_depth=4, max_features=6;, score=0.788 total time= 0.0s
[CV 3/5; 27/70] START max_depth=4, max_features=6...............................
[CV 3/5; 27/70] END max_depth=4, max_features=6;, score=0.791 total time= 0.0s
[CV 4/5; 27/70] START max_depth=4, max_features=6...............................
[CV 4/5; 27/70] END max_depth=4, max_features=6;, score=0.789 total time= 0.0s
[CV 5/5; 27/70] START max_depth=4, max_features=6...............................
[CV 5/5; 27/70] END max_depth=4, max_features=6;, score=0.780 total time= 0.0s
[CV 1/5; 28/70] START max_depth=4, max_features=7...............................
[CV 1/5; 28/70] END max_depth=4, max_features=7;, score=0.786 total time= 0.0s
[CV 2/5; 28/70] START max_depth=4, max_features=7...............................
[CV 2/5; 28/70] END max_depth=4, max_features=7;, score=0.791 total time= 0.0s
[CV 3/5; 28/70] START max_depth=4, max_features=7...............................
[CV 3/5; 28/70] END max_depth=4, max_features=7;, score=0.780 total time= 0.0s
[CV 4/5; 28/70] START max_depth=4, max_features=7...............................
[CV 4/5; 28/70] END max_depth=4, max_features=7;, score=0.797 total time= 0.0s
[CV 5/5; 28/70] START max_depth=4, max_features=7...............................
[CV 5/5; 28/70] END max_depth=4, max_features=7;, score=0.773 total time= 0.0s
[CV 1/5; 29/70] START max_depth=5, max_features=1...............................
[CV 1/5; 29/70] END max_depth=5, max_features=1;, score=0.800 total time= 0.0s
[CV 2/5; 29/70] START max_depth=5, max_features=1...............................
[CV 2/5; 29/70] END max_depth=5, max_features=1;, score=0.765 total time= 0.0s
[CV 3/5; 29/70] START max_depth=5, max_features=1...............................
[CV 3/5; 29/70] END max_depth=5, max_features=1;, score=0.682 total time= 0.0s
[CV 4/5; 29/70] START max_depth=5, max_features=1...............................
[CV 4/5; 29/70] END max_depth=5, max_features=1;, score=0.664 total time= 0.0s
[CV 5/5; 29/70] START max_depth=5, max_features=1...............................
[CV 5/5; 29/70] END max_depth=5, max_features=1;, score=0.740 total time= 0.0s
[CV 1/5; 30/70] START max_depth=5, max_features=2...............................
[CV 1/5; 30/70] END max_depth=5, max_features=2;, score=0.816 total time= 0.0s
[CV 2/5; 30/70] START max_depth=5, max_features=2...............................
[CV 2/5; 30/70] END max_depth=5, max_features=2;, score=0.650 total time= 0.0s
[CV 3/5; 30/70] START max_depth=5, max_features=2...............................
[CV 3/5; 30/70] END max_depth=5, max_features=2;, score=0.739 total time= 0.0s
[CV 4/5; 30/70] START max_depth=5, max_features=2...............................
[CV 4/5; 30/70] END max_depth=5, max_features=2;, score=0.766 total time= 0.0s
[CV 5/5; 30/70] START max_depth=5, max_features=2...............................
[CV 5/5; 30/70] END max_depth=5, max_features=2;, score=0.771 total time= 0.0s
[CV 1/5; 31/70] START max_depth=5, max_features=3...............................
[CV 1/5; 31/70] END max_depth=5, max_features=3;, score=0.799 total time= 0.0s
[CV 2/5; 31/70] START max_depth=5, max_features=3...............................
[CV 2/5; 31/70] END max_depth=5, max_features=3;, score=0.798 total time= 0.0s
[CV 3/5; 31/70] START max_depth=5, max_features=3...............................
[CV 3/5; 31/70] END max_depth=5, max_features=3;, score=0.678 total time= 0.0s
[CV 4/5; 31/70] START max_depth=5, max_features=3...............................
[CV 4/5; 31/70] END max_depth=5, max_features=3;, score=0.800 total time= 0.0s
[CV 5/5; 31/70] START max_depth=5, max_features=3...............................
[CV 5/5; 31/70] END max_depth=5, max_features=3;, score=0.757 total time= 0.0s
[CV 1/5; 32/70] START max_depth=5, max_features=4...............................
[CV 1/5; 32/70] END max_depth=5, max_features=4;, score=0.801 total time= 0.0s
[CV 2/5; 32/70] START max_depth=5, max_features=4...............................
[CV 2/5; 32/70] END max_depth=5, max_features=4;, score=0.793 total time= 0.0s
[CV 3/5; 32/70] START max_depth=5, max_features=4...............................
[CV 3/5; 32/70] END max_depth=5, max_features=4;, score=0.782 total time= 0.0s
[CV 4/5; 32/70] START max_depth=5, max_features=4...............................
[CV 4/5; 32/70] END max_depth=5, max_features=4;, score=0.794 total time= 0.0s
[CV 5/5; 32/70] START max_depth=5, max_features=4...............................
[CV 5/5; 32/70] END max_depth=5, max_features=4;, score=0.785 total time= 0.0s
[CV 1/5; 33/70] START max_depth=5, max_features=5...............................
[CV 1/5; 33/70] END max_depth=5, max_features=5;, score=0.794 total time= 0.0s
[CV 2/5; 33/70] START max_depth=5, max_features=5...............................
[CV 2/5; 33/70] END max_depth=5, max_features=5;, score=0.789 total time= 0.0s
[CV 3/5; 33/70] START max_depth=5, max_features=5...............................
[CV 3/5; 33/70] END max_depth=5, max_features=5;, score=0.790 total time= 0.0s
[CV 4/5; 33/70] START max_depth=5, max_features=5...............................
[CV 4/5; 33/70] END max_depth=5, max_features=5;, score=0.801 total time= 0.0s
[CV 5/5; 33/70] START max_depth=5, max_features=5...............................
[CV 5/5; 33/70] END max_depth=5, max_features=5;, score=0.785 total time= 0.0s
[CV 1/5; 34/70] START max_depth=5, max_features=6...............................
[CV 1/5; 34/70] END max_depth=5, max_features=6;, score=0.800 total time= 0.0s
[CV 2/5; 34/70] START max_depth=5, max_features=6...............................
[CV 2/5; 34/70] END max_depth=5, max_features=6;, score=0.793 total time= 0.0s
[CV 3/5; 34/70] START max_depth=5, max_features=6...............................
[CV 3/5; 34/70] END max_depth=5, max_features=6;, score=0.798 total time= 0.0s
[CV 4/5; 34/70] START max_depth=5, max_features=6...............................
[CV 4/5; 34/70] END max_depth=5, max_features=6;, score=0.796 total time= 0.0s
[CV 5/5; 34/70] START max_depth=5, max_features=6...............................
[CV 5/5; 34/70] END max_depth=5, max_features=6;, score=0.776 total time= 0.0s
[CV 1/5; 35/70] START max_depth=5, max_features=7...............................
[CV 1/5; 35/70] END max_depth=5, max_features=7;, score=0.813 total time= 0.0s
[CV 2/5; 35/70] START max_depth=5, max_features=7...............................
[CV 2/5; 35/70] END max_depth=5, max_features=7;, score=0.805 total time= 0.0s
[CV 3/5; 35/70] START max_depth=5, max_features=7...............................
[CV 3/5; 35/70] END max_depth=5, max_features=7;, score=0.786 total time= 0.0s
[CV 4/5; 35/70] START max_depth=5, max_features=7...............................
[CV 4/5; 35/70] END max_depth=5, max_features=7;, score=0.807 total time= 0.0s
[CV 5/5; 35/70] START max_depth=5, max_features=7...............................
[CV 5/5; 35/70] END max_depth=5, max_features=7;, score=0.800 total time= 0.0s
[CV 1/5; 36/70] START max_depth=6, max_features=1...............................
[CV 1/5; 36/70] END max_depth=6, max_features=1;, score=0.780 total time= 0.0s
[CV 2/5; 36/70] START max_depth=6, max_features=1...............................
[CV 2/5; 36/70] END max_depth=6, max_features=1;, score=0.760 total time= 0.0s
[CV 3/5; 36/70] START max_depth=6, max_features=1...............................
[CV 3/5; 36/70] END max_depth=6, max_features=1;, score=0.785 total time= 0.0s
[CV 4/5; 36/70] START max_depth=6, max_features=1...............................
[CV 4/5; 36/70] END max_depth=6, max_features=1;, score=0.802 total time= 0.0s
[CV 5/5; 36/70] START max_depth=6, max_features=1...............................
[CV 5/5; 36/70] END max_depth=6, max_features=1;, score=0.688 total time= 0.0s
[CV 1/5; 37/70] START max_depth=6, max_features=2...............................
[CV 1/5; 37/70] END max_depth=6, max_features=2;, score=0.770 total time= 0.0s
[CV 2/5; 37/70] START max_depth=6, max_features=2...............................
[CV 2/5; 37/70] END max_depth=6, max_features=2;, score=0.796 total time= 0.0s
[CV 3/5; 37/70] START max_depth=6, max_features=2...............................
[CV 3/5; 37/70] END max_depth=6, max_features=2;, score=0.797 total time= 0.0s
[CV 4/5; 37/70] START max_depth=6, max_features=2...............................
[CV 4/5; 37/70] END max_depth=6, max_features=2;, score=0.792 total time= 0.0s
[CV 5/5; 37/70] START max_depth=6, max_features=2...............................
[CV 5/5; 37/70] END max_depth=6, max_features=2;, score=0.772 total time= 0.0s
[CV 1/5; 38/70] START max_depth=6, max_features=3...............................
[CV 1/5; 38/70] END max_depth=6, max_features=3;, score=0.814 total time= 0.0s
[CV 2/5; 38/70] START max_depth=6, max_features=3...............................
[CV 2/5; 38/70] END max_depth=6, max_features=3;, score=0.781 total time= 0.0s
[CV 3/5; 38/70] START max_depth=6, max_features=3...............................
[CV 3/5; 38/70] END max_depth=6, max_features=3;, score=0.733 total time= 0.0s
[CV 4/5; 38/70] START max_depth=6, max_features=3...............................
[CV 4/5; 38/70] END max_depth=6, max_features=3;, score=0.783 total time= 0.0s
[CV 5/5; 38/70] START max_depth=6, max_features=3...............................
[CV 5/5; 38/70] END max_depth=6, max_features=3;, score=0.784 total time= 0.0s
[CV 1/5; 39/70] START max_depth=6, max_features=4...............................
[CV 1/5; 39/70] END max_depth=6, max_features=4;, score=0.811 total time= 0.0s
[CV 2/5; 39/70] START max_depth=6, max_features=4...............................
[CV 2/5; 39/70] END max_depth=6, max_features=4;, score=0.805 total time= 0.0s
[CV 3/5; 39/70] START max_depth=6, max_features=4...............................
[CV 3/5; 39/70] END max_depth=6, max_features=4;, score=0.805 total time= 0.0s
[CV 4/5; 39/70] START max_depth=6, max_features=4...............................
[CV 4/5; 39/70] END max_depth=6, max_features=4;, score=0.827 total time= 0.0s
[CV 5/5; 39/70] START max_depth=6, max_features=4...............................
[CV 5/5; 39/70] END max_depth=6, max_features=4;, score=0.789 total time= 0.0s
[CV 1/5; 40/70] START max_depth=6, max_features=5...............................
[CV 1/5; 40/70] END max_depth=6, max_features=5;, score=0.819 total time= 0.0s
[CV 2/5; 40/70] START max_depth=6, max_features=5...............................
[CV 2/5; 40/70] END max_depth=6, max_features=5;, score=0.807 total time= 0.0s
[CV 3/5; 40/70] START max_depth=6, max_features=5...............................
[CV 3/5; 40/70] END max_depth=6, max_features=5;, score=0.789 total time= 0.0s
[CV 4/5; 40/70] START max_depth=6, max_features=5...............................
[CV 4/5; 40/70] END max_depth=6, max_features=5;, score=0.811 total time= 0.0s
[CV 5/5; 40/70] START max_depth=6, max_features=5...............................
[CV 5/5; 40/70] END max_depth=6, max_features=5;, score=0.792 total time= 0.0s
[CV 1/5; 41/70] START max_depth=6, max_features=6...............................
[CV 1/5; 41/70] END max_depth=6, max_features=6;, score=0.805 total time= 0.0s
[CV 2/5; 41/70] START max_depth=6, max_features=6...............................
[CV 2/5; 41/70] END max_depth=6, max_features=6;, score=0.822 total time= 0.0s
[CV 3/5; 41/70] START max_depth=6, max_features=6...............................
[CV 3/5; 41/70] END max_depth=6, max_features=6;, score=0.794 total time= 0.0s
[CV 4/5; 41/70] START max_depth=6, max_features=6...............................
[CV 4/5; 41/70] END max_depth=6, max_features=6;, score=0.810 total time= 0.0s
[CV 5/5; 41/70] START max_depth=6, max_features=6...............................
[CV 5/5; 41/70] END max_depth=6, max_features=6;, score=0.796 total time= 0.0s
[CV 1/5; 42/70] START max_depth=6, max_features=7...............................
[CV 1/5; 42/70] END max_depth=6, max_features=7;, score=0.803 total time= 0.0s
[CV 2/5; 42/70] START max_depth=6, max_features=7...............................
[CV 2/5; 42/70] END max_depth=6, max_features=7;, score=0.822 total time= 0.0s
[CV 3/5; 42/70] START max_depth=6, max_features=7...............................
[CV 3/5; 42/70] END max_depth=6, max_features=7;, score=0.812 total time= 0.0s
[CV 4/5; 42/70] START max_depth=6, max_features=7...............................
[CV 4/5; 42/70] END max_depth=6, max_features=7;, score=0.819 total time= 0.0s
[CV 5/5; 42/70] START max_depth=6, max_features=7...............................
[CV 5/5; 42/70] END max_depth=6, max_features=7;, score=0.796 total time= 0.0s
[CV 1/5; 43/70] START max_depth=7, max_features=1...............................
[CV 1/5; 43/70] END max_depth=7, max_features=1;, score=0.677 total time= 0.0s
[CV 2/5; 43/70] START max_depth=7, max_features=1...............................
[CV 2/5; 43/70] END max_depth=7, max_features=1;, score=0.762 total time= 0.0s
[CV 3/5; 43/70] START max_depth=7, max_features=1...............................
[CV 3/5; 43/70] END max_depth=7, max_features=1;, score=0.780 total time= 0.0s
[CV 4/5; 43/70] START max_depth=7, max_features=1...............................
[CV 4/5; 43/70] END max_depth=7, max_features=1;, score=0.783 total time= 0.0s
[CV 5/5; 43/70] START max_depth=7, max_features=1...............................
[CV 5/5; 43/70] END max_depth=7, max_features=1;, score=0.677 total time= 0.0s
[CV 1/5; 44/70] START max_depth=7, max_features=2...............................
[CV 1/5; 44/70] END max_depth=7, max_features=2;, score=0.812 total time= 0.0s
[CV 2/5; 44/70] START max_depth=7, max_features=2...............................
[CV 2/5; 44/70] END max_depth=7, max_features=2;, score=0.812 total time= 0.0s
[CV 3/5; 44/70] START max_depth=7, max_features=2...............................
[CV 3/5; 44/70] END max_depth=7, max_features=2;, score=0.804 total time= 0.0s
[CV 4/5; 44/70] START max_depth=7, max_features=2...............................
[CV 4/5; 44/70] END max_depth=7, max_features=2;, score=0.805 total time= 0.0s
[CV 5/5; 44/70] START max_depth=7, max_features=2...............................
[CV 5/5; 44/70] END max_depth=7, max_features=2;, score=0.790 total time= 0.0s
[CV 1/5; 45/70] START max_depth=7, max_features=3...............................
[CV 1/5; 45/70] END max_depth=7, max_features=3;, score=0.812 total time= 0.0s
[CV 2/5; 45/70] START max_depth=7, max_features=3...............................
[CV 2/5; 45/70] END max_depth=7, max_features=3;, score=0.784 total time= 0.0s
[CV 3/5; 45/70] START max_depth=7, max_features=3...............................
[CV 3/5; 45/70] END max_depth=7, max_features=3;, score=0.788 total time= 0.0s
[CV 4/5; 45/70] START max_depth=7, max_features=3...............................
[CV 4/5; 45/70] END max_depth=7, max_features=3;, score=0.802 total time= 0.0s
[CV 5/5; 45/70] START max_depth=7, max_features=3...............................
[CV 5/5; 45/70] END max_depth=7, max_features=3;, score=0.807 total time= 0.0s
[CV 1/5; 46/70] START max_depth=7, max_features=4...............................
[CV 1/5; 46/70] END max_depth=7, max_features=4;, score=0.806 total time= 0.0s
[CV 2/5; 46/70] START max_depth=7, max_features=4...............................
[CV 2/5; 46/70] END max_depth=7, max_features=4;, score=0.810 total time= 0.0s
[CV 3/5; 46/70] START max_depth=7, max_features=4...............................
[CV 3/5; 46/70] END max_depth=7, max_features=4;, score=0.811 total time= 0.0s
[CV 4/5; 46/70] START max_depth=7, max_features=4...............................
[CV 4/5; 46/70] END max_depth=7, max_features=4;, score=0.825 total time= 0.0s
[CV 5/5; 46/70] START max_depth=7, max_features=4...............................
[CV 5/5; 46/70] END max_depth=7, max_features=4;, score=0.808 total time= 0.0s
[CV 1/5; 47/70] START max_depth=7, max_features=5...............................
[CV 1/5; 47/70] END max_depth=7, max_features=5;, score=0.824 total time= 0.0s
[CV 2/5; 47/70] START max_depth=7, max_features=5...............................
[CV 2/5; 47/70] END max_depth=7, max_features=5;, score=0.822 total time= 0.0s
[CV 3/5; 47/70] START max_depth=7, max_features=5...............................
[CV 3/5; 47/70] END max_depth=7, max_features=5;, score=0.802 total time= 0.0s
[CV 4/5; 47/70] START max_depth=7, max_features=5...............................
[CV 4/5; 47/70] END max_depth=7, max_features=5;, score=0.818 total time= 0.0s
[CV 5/5; 47/70] START max_depth=7, max_features=5...............................
[CV 5/5; 47/70] END max_depth=7, max_features=5;, score=0.811 total time= 0.0s
[CV 1/5; 48/70] START max_depth=7, max_features=6...............................
[CV 1/5; 48/70] END max_depth=7, max_features=6;, score=0.827 total time= 0.0s
[CV 2/5; 48/70] START max_depth=7, max_features=6...............................
[CV 2/5; 48/70] END max_depth=7, max_features=6;, score=0.819 total time= 0.0s
[CV 3/5; 48/70] START max_depth=7, max_features=6...............................
[CV 3/5; 48/70] END max_depth=7, max_features=6;, score=0.824 total time= 0.0s
[CV 4/5; 48/70] START max_depth=7, max_features=6...............................
[CV 4/5; 48/70] END max_depth=7, max_features=6;, score=0.822 total time= 0.0s
[CV 5/5; 48/70] START max_depth=7, max_features=6...............................
[CV 5/5; 48/70] END max_depth=7, max_features=6;, score=0.816 total time= 0.0s
[CV 1/5; 49/70] START max_depth=7, max_features=7...............................
[CV 1/5; 49/70] END max_depth=7, max_features=7;, score=0.827 total time= 0.0s
[CV 2/5; 49/70] START max_depth=7, max_features=7...............................
[CV 2/5; 49/70] END max_depth=7, max_features=7;, score=0.815 total time= 0.0s
[CV 3/5; 49/70] START max_depth=7, max_features=7...............................
[CV 3/5; 49/70] END max_depth=7, max_features=7;, score=0.821 total time= 0.0s
[CV 4/5; 49/70] START max_depth=7, max_features=7...............................
[CV 4/5; 49/70] END max_depth=7, max_features=7;, score=0.835 total time= 0.0s
[CV 5/5; 49/70] START max_depth=7, max_features=7...............................
[CV 5/5; 49/70] END max_depth=7, max_features=7;, score=0.821 total time= 0.0s
[CV 1/5; 50/70] START max_depth=8, max_features=1...............................
[CV 1/5; 50/70] END max_depth=8, max_features=1;, score=0.732 total time= 0.0s
[CV 2/5; 50/70] START max_depth=8, max_features=1...............................
[CV 2/5; 50/70] END max_depth=8, max_features=1;, score=0.798 total time= 0.0s
[CV 3/5; 50/70] START max_depth=8, max_features=1...............................
[CV 3/5; 50/70] END max_depth=8, max_features=1;, score=0.799 total time= 0.0s
[CV 4/5; 50/70] START max_depth=8, max_features=1...............................
[CV 4/5; 50/70] END max_depth=8, max_features=1;, score=0.783 total time= 0.0s
[CV 5/5; 50/70] START max_depth=8, max_features=1...............................
[CV 5/5; 50/70] END max_depth=8, max_features=1;, score=0.782 total time= 0.0s
[CV 1/5; 51/70] START max_depth=8, max_features=2...............................
[CV 1/5; 51/70] END max_depth=8, max_features=2;, score=0.731 total time= 0.0s
[CV 2/5; 51/70] START max_depth=8, max_features=2...............................
[CV 2/5; 51/70] END max_depth=8, max_features=2;, score=0.812 total time= 0.0s
[CV 3/5; 51/70] START max_depth=8, max_features=2...............................
[CV 3/5; 51/70] END max_depth=8, max_features=2;, score=0.799 total time= 0.0s
[CV 4/5; 51/70] START max_depth=8, max_features=2...............................
[CV 4/5; 51/70] END max_depth=8, max_features=2;, score=0.790 total time= 0.0s
[CV 5/5; 51/70] START max_depth=8, max_features=2...............................
[CV 5/5; 51/70] END max_depth=8, max_features=2;, score=0.785 total time= 0.0s
[CV 1/5; 52/70] START max_depth=8, max_features=3...............................
[CV 1/5; 52/70] END max_depth=8, max_features=3;, score=0.817 total time= 0.0s
[CV 2/5; 52/70] START max_depth=8, max_features=3...............................
[CV 2/5; 52/70] END max_depth=8, max_features=3;, score=0.821 total time= 0.0s
[CV 3/5; 52/70] START max_depth=8, max_features=3...............................
[CV 3/5; 52/70] END max_depth=8, max_features=3;, score=0.799 total time= 0.0s
[CV 4/5; 52/70] START max_depth=8, max_features=3...............................
[CV 4/5; 52/70] END max_depth=8, max_features=3;, score=0.819 total time= 0.0s
[CV 5/5; 52/70] START max_depth=8, max_features=3...............................
[CV 5/5; 52/70] END max_depth=8, max_features=3;, score=0.779 total time= 0.0s
[CV 1/5; 53/70] START max_depth=8, max_features=4...............................
[CV 1/5; 53/70] END max_depth=8, max_features=4;, score=0.826 total time= 0.0s
[CV 2/5; 53/70] START max_depth=8, max_features=4...............................
[CV 2/5; 53/70] END max_depth=8, max_features=4;, score=0.830 total time= 0.0s
[CV 3/5; 53/70] START max_depth=8, max_features=4...............................
[CV 3/5; 53/70] END max_depth=8, max_features=4;, score=0.820 total time= 0.0s
[CV 4/5; 53/70] START max_depth=8, max_features=4...............................
[CV 4/5; 53/70] END max_depth=8, max_features=4;, score=0.807 total time= 0.0s
[CV 5/5; 53/70] START max_depth=8, max_features=4...............................
[CV 5/5; 53/70] END max_depth=8, max_features=4;, score=0.805 total time= 0.0s
[CV 1/5; 54/70] START max_depth=8, max_features=5...............................
[CV 1/5; 54/70] END max_depth=8, max_features=5;, score=0.828 total time= 0.0s
[CV 2/5; 54/70] START max_depth=8, max_features=5...............................
[CV 2/5; 54/70] END max_depth=8, max_features=5;, score=0.816 total time= 0.0s
[CV 3/5; 54/70] START max_depth=8, max_features=5...............................
[CV 3/5; 54/70] END max_depth=8, max_features=5;, score=0.820 total time= 0.0s
[CV 4/5; 54/70] START max_depth=8, max_features=5...............................
[CV 4/5; 54/70] END max_depth=8, max_features=5;, score=0.821 total time= 0.0s
[CV 5/5; 54/70] START max_depth=8, max_features=5...............................
[CV 5/5; 54/70] END max_depth=8, max_features=5;, score=0.821 total time= 0.0s
[CV 1/5; 55/70] START max_depth=8, max_features=6...............................
[CV 1/5; 55/70] END max_depth=8, max_features=6;, score=0.831 total time= 0.0s
[CV 2/5; 55/70] START max_depth=8, max_features=6...............................
[CV 2/5; 55/70] END max_depth=8, max_features=6;, score=0.819 total time= 0.0s
[CV 3/5; 55/70] START max_depth=8, max_features=6...............................
[CV 3/5; 55/70] END max_depth=8, max_features=6;, score=0.828 total time= 0.0s
[CV 4/5; 55/70] START max_depth=8, max_features=6...............................
[CV 4/5; 55/70] END max_depth=8, max_features=6;, score=0.830 total time= 0.0s
[CV 5/5; 55/70] START max_depth=8, max_features=6...............................
[CV 5/5; 55/70] END max_depth=8, max_features=6;, score=0.815 total time= 0.0s
[CV 1/5; 56/70] START max_depth=8, max_features=7...............................
[CV 1/5; 56/70] END max_depth=8, max_features=7;, score=0.821 total time= 0.0s
[CV 2/5; 56/70] START max_depth=8, max_features=7...............................
[CV 2/5; 56/70] END max_depth=8, max_features=7;, score=0.821 total time= 0.0s
[CV 3/5; 56/70] START max_depth=8, max_features=7...............................
[CV 3/5; 56/70] END max_depth=8, max_features=7;, score=0.821 total time= 0.0s
[CV 4/5; 56/70] START max_depth=8, max_features=7...............................
[CV 4/5; 56/70] END max_depth=8, max_features=7;, score=0.830 total time= 0.0s
[CV 5/5; 56/70] START max_depth=8, max_features=7...............................
[CV 5/5; 56/70] END max_depth=8, max_features=7;, score=0.826 total time= 0.0s
[CV 1/5; 57/70] START max_depth=9, max_features=1...............................
[CV 1/5; 57/70] END max_depth=9, max_features=1;, score=0.827 total time= 0.0s
[CV 2/5; 57/70] START max_depth=9, max_features=1...............................
[CV 2/5; 57/70] END max_depth=9, max_features=1;, score=0.776 total time= 0.0s
[CV 3/5; 57/70] START max_depth=9, max_features=1...............................
[CV 3/5; 57/70] END max_depth=9, max_features=1;, score=0.726 total time= 0.0s
[CV 4/5; 57/70] START max_depth=9, max_features=1...............................
[CV 4/5; 57/70] END max_depth=9, max_features=1;, score=0.802 total time= 0.0s
[CV 5/5; 57/70] START max_depth=9, max_features=1...............................
[CV 5/5; 57/70] END max_depth=9, max_features=1;, score=0.797 total time= 0.0s
[CV 1/5; 58/70] START max_depth=9, max_features=2...............................
[CV 1/5; 58/70] END max_depth=9, max_features=2;, score=0.799 total time= 0.0s
[CV 2/5; 58/70] START max_depth=9, max_features=2...............................
[CV 2/5; 58/70] END max_depth=9, max_features=2;, score=0.827 total time= 0.0s
[CV 3/5; 58/70] START max_depth=9, max_features=2...............................
[CV 3/5; 58/70] END max_depth=9, max_features=2;, score=0.802 total time= 0.0s
[CV 4/5; 58/70] START max_depth=9, max_features=2...............................
[CV 4/5; 58/70] END max_depth=9, max_features=2;, score=0.825 total time= 0.0s
[CV 5/5; 58/70] START max_depth=9, max_features=2...............................
[CV 5/5; 58/70] END max_depth=9, max_features=2;, score=0.826 total time= 0.0s
[CV 1/5; 59/70] START max_depth=9, max_features=3...............................
[CV 1/5; 59/70] END max_depth=9, max_features=3;, score=0.844 total time= 0.0s
[CV 2/5; 59/70] START max_depth=9, max_features=3...............................
[CV 2/5; 59/70] END max_depth=9, max_features=3;, score=0.820 total time= 0.0s
[CV 3/5; 59/70] START max_depth=9, max_features=3...............................
[CV 3/5; 59/70] END max_depth=9, max_features=3;, score=0.831 total time= 0.0s
[CV 4/5; 59/70] START max_depth=9, max_features=3...............................
[CV 4/5; 59/70] END max_depth=9, max_features=3;, score=0.816 total time= 0.0s
[CV 5/5; 59/70] START max_depth=9, max_features=3...............................
[CV 5/5; 59/70] END max_depth=9, max_features=3;, score=0.818 total time= 0.0s
[CV 1/5; 60/70] START max_depth=9, max_features=4...............................
[CV 1/5; 60/70] END max_depth=9, max_features=4;, score=0.823 total time= 0.0s
[CV 2/5; 60/70] START max_depth=9, max_features=4...............................
[CV 2/5; 60/70] END max_depth=9, max_features=4;, score=0.825 total time= 0.0s
[CV 3/5; 60/70] START max_depth=9, max_features=4...............................
[CV 3/5; 60/70] END max_depth=9, max_features=4;, score=0.825 total time= 0.0s
[CV 4/5; 60/70] START max_depth=9, max_features=4...............................
[CV 4/5; 60/70] END max_depth=9, max_features=4;, score=0.827 total time= 0.0s
[CV 5/5; 60/70] START max_depth=9, max_features=4...............................
[CV 5/5; 60/70] END max_depth=9, max_features=4;, score=0.830 total time= 0.0s
[CV 1/5; 61/70] START max_depth=9, max_features=5...............................
[CV 1/5; 61/70] END max_depth=9, max_features=5;, score=0.840 total time= 0.0s
[CV 2/5; 61/70] START max_depth=9, max_features=5...............................
[CV 2/5; 61/70] END max_depth=9, max_features=5;, score=0.833 total time= 0.0s
[CV 3/5; 61/70] START max_depth=9, max_features=5...............................
[CV 3/5; 61/70] END max_depth=9, max_features=5;, score=0.836 total time= 0.0s
[CV 4/5; 61/70] START max_depth=9, max_features=5...............................
[CV 4/5; 61/70] END max_depth=9, max_features=5;, score=0.838 total time= 0.0s
[CV 5/5; 61/70] START max_depth=9, max_features=5...............................
[CV 5/5; 61/70] END max_depth=9, max_features=5;, score=0.827 total time= 0.0s
[CV 1/5; 62/70] START max_depth=9, max_features=6...............................
[CV 1/5; 62/70] END max_depth=9, max_features=6;, score=0.829 total time= 0.0s
[CV 2/5; 62/70] START max_depth=9, max_features=6...............................
[CV 2/5; 62/70] END max_depth=9, max_features=6;, score=0.822 total time= 0.0s
[CV 3/5; 62/70] START max_depth=9, max_features=6...............................
[CV 3/5; 62/70] END max_depth=9, max_features=6;, score=0.846 total time= 0.0s
[CV 4/5; 62/70] START max_depth=9, max_features=6...............................
[CV 4/5; 62/70] END max_depth=9, max_features=6;, score=0.836 total time= 0.0s
[CV 5/5; 62/70] START max_depth=9, max_features=6...............................
[CV 5/5; 62/70] END max_depth=9, max_features=6;, score=0.829 total time= 0.0s
[CV 1/5; 63/70] START max_depth=9, max_features=7...............................
[CV 1/5; 63/70] END max_depth=9, max_features=7;, score=0.846 total time= 0.0s
[CV 2/5; 63/70] START max_depth=9, max_features=7...............................
[CV 2/5; 63/70] END max_depth=9, max_features=7;, score=0.832 total time= 0.0s
[CV 3/5; 63/70] START max_depth=9, max_features=7...............................
[CV 3/5; 63/70] END max_depth=9, max_features=7;, score=0.830 total time= 0.0s
[CV 4/5; 63/70] START max_depth=9, max_features=7...............................
[CV 4/5; 63/70] END max_depth=9, max_features=7;, score=0.849 total time= 0.0s
[CV 5/5; 63/70] START max_depth=9, max_features=7...............................
[CV 5/5; 63/70] END max_depth=9, max_features=7;, score=0.831 total time= 0.0s
[CV 1/5; 64/70] START max_depth=10, max_features=1..............................
[CV 1/5; 64/70] END max_depth=10, max_features=1;, score=0.805 total time= 0.0s
[CV 2/5; 64/70] START max_depth=10, max_features=1..............................
[CV 2/5; 64/70] END max_depth=10, max_features=1;, score=0.819 total time= 0.0s
[CV 3/5; 64/70] START max_depth=10, max_features=1..............................
[CV 3/5; 64/70] END max_depth=10, max_features=1;, score=0.832 total time= 0.0s
[CV 4/5; 64/70] START max_depth=10, max_features=1..............................
[CV 4/5; 64/70] END max_depth=10, max_features=1;, score=0.819 total time= 0.0s
[CV 5/5; 64/70] START max_depth=10, max_features=1..............................
[CV 5/5; 64/70] END max_depth=10, max_features=1;, score=0.799 total time= 0.0s
[CV 1/5; 65/70] START max_depth=10, max_features=2..............................
[CV 1/5; 65/70] END max_depth=10, max_features=2;, score=0.820 total time= 0.0s
[CV 2/5; 65/70] START max_depth=10, max_features=2..............................
[CV 2/5; 65/70] END max_depth=10, max_features=2;, score=0.825 total time= 0.0s
[CV 3/5; 65/70] START max_depth=10, max_features=2..............................
[CV 3/5; 65/70] END max_depth=10, max_features=2;, score=0.807 total time= 0.0s
[CV 4/5; 65/70] START max_depth=10, max_features=2..............................
[CV 4/5; 65/70] END max_depth=10, max_features=2;, score=0.821 total time= 0.0s
[CV 5/5; 65/70] START max_depth=10, max_features=2..............................
[CV 5/5; 65/70] END max_depth=10, max_features=2;, score=0.802 total time= 0.0s
[CV 1/5; 66/70] START max_depth=10, max_features=3..............................
[CV 1/5; 66/70] END max_depth=10, max_features=3;, score=0.838 total time= 0.0s
[CV 2/5; 66/70] START max_depth=10, max_features=3..............................
[CV 2/5; 66/70] END max_depth=10, max_features=3;, score=0.823 total time= 0.0s
[CV 3/5; 66/70] START max_depth=10, max_features=3..............................
[CV 3/5; 66/70] END max_depth=10, max_features=3;, score=0.844 total time= 0.0s
[CV 4/5; 66/70] START max_depth=10, max_features=3..............................
[CV 4/5; 66/70] END max_depth=10, max_features=3;, score=0.851 total time= 0.0s
[CV 5/5; 66/70] START max_depth=10, max_features=3..............................
[CV 5/5; 66/70] END max_depth=10, max_features=3;, score=0.832 total time= 0.0s
[CV 1/5; 67/70] START max_depth=10, max_features=4..............................
[CV 1/5; 67/70] END max_depth=10, max_features=4;, score=0.831 total time= 0.0s
[CV 2/5; 67/70] START max_depth=10, max_features=4..............................
[CV 2/5; 67/70] END max_depth=10, max_features=4;, score=0.832 total time= 0.0s
[CV 3/5; 67/70] START max_depth=10, max_features=4..............................
[CV 3/5; 67/70] END max_depth=10, max_features=4;, score=0.827 total time= 0.0s
[CV 4/5; 67/70] START max_depth=10, max_features=4..............................
[CV 4/5; 67/70] END max_depth=10, max_features=4;, score=0.852 total time= 0.0s
[CV 5/5; 67/70] START max_depth=10, max_features=4..............................
[CV 5/5; 67/70] END max_depth=10, max_features=4;, score=0.831 total time= 0.0s
[CV 1/5; 68/70] START max_depth=10, max_features=5..............................
[CV 1/5; 68/70] END max_depth=10, max_features=5;, score=0.847 total time= 0.0s
[CV 2/5; 68/70] START max_depth=10, max_features=5..............................
[CV 2/5; 68/70] END max_depth=10, max_features=5;, score=0.854 total time= 0.0s
[CV 3/5; 68/70] START max_depth=10, max_features=5..............................
[CV 3/5; 68/70] END max_depth=10, max_features=5;, score=0.826 total time= 0.0s
[CV 4/5; 68/70] START max_depth=10, max_features=5..............................
[CV 4/5; 68/70] END max_depth=10, max_features=5;, score=0.846 total time= 0.0s
[CV 5/5; 68/70] START max_depth=10, max_features=5..............................
[CV 5/5; 68/70] END max_depth=10, max_features=5;, score=0.811 total time= 0.0s
[CV 1/5; 69/70] START max_depth=10, max_features=6..............................
[CV 1/5; 69/70] END max_depth=10, max_features=6;, score=0.848 total time= 0.0s
[CV 2/5; 69/70] START max_depth=10, max_features=6..............................
[CV 2/5; 69/70] END max_depth=10, max_features=6;, score=0.846 total time= 0.0s
[CV 3/5; 69/70] START max_depth=10, max_features=6..............................
[CV 3/5; 69/70] END max_depth=10, max_features=6;, score=0.846 total time= 0.0s
[CV 4/5; 69/70] START max_depth=10, max_features=6..............................
[CV 4/5; 69/70] END max_depth=10, max_features=6;, score=0.846 total time= 0.0s
[CV 5/5; 69/70] START max_depth=10, max_features=6..............................
[CV 5/5; 69/70] END max_depth=10, max_features=6;, score=0.839 total time= 0.0s
[CV 1/5; 70/70] START max_depth=10, max_features=7..............................
[CV 1/5; 70/70] END max_depth=10, max_features=7;, score=0.835 total time= 0.0s
[CV 2/5; 70/70] START max_depth=10, max_features=7..............................
[CV 2/5; 70/70] END max_depth=10, max_features=7;, score=0.843 total time= 0.0s
[CV 3/5; 70/70] START max_depth=10, max_features=7..............................
[CV 3/5; 70/70] END max_depth=10, max_features=7;, score=0.846 total time= 0.0s
[CV 4/5; 70/70] START max_depth=10, max_features=7..............................
[CV 4/5; 70/70] END max_depth=10, max_features=7;, score=0.853 total time= 0.0s
[CV 5/5; 70/70] START max_depth=10, max_features=7..............................
[CV 5/5; 70/70] END max_depth=10, max_features=7;, score=0.836 total time= 0.0s
VOILA LES MEILLEURS PARAMÉTRES : {'max_depth': 10, 'max_features': 6}
Out[ ]:
DecisionTreeClassifier(max_depth=10, max_features=6)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
DecisionTreeClassifier(max_depth=10, max_features=6)
Prédiction¶
In [ ]:
# Prédiction sur la base test
best_Y_test_pred_AD = best_modele_AD.predict(X_test)
best_Y_test_pred_AD
Out[ ]:
array([1, 0, 0, ..., 0, 0, 1])
In [ ]:
# Prédiction sur la base train
best_Y_train_pred_AD = best_modele_AD.predict(X_train)
best_Y_train_pred_AD
Out[ ]:
array([1, 1, 1, ..., 1, 0, 1])
In [ ]:
# prediction en proba sur la base train
Y_pred_train_proba_AD = best_modele_AD.predict_proba(X_train)
Y_pred_train_proba_AD
Out[ ]:
array([[0.09840675, 0.90159325],
[0.3015873 , 0.6984127 ],
[0.18726592, 0.81273408],
...,
[0.09840675, 0.90159325],
[1. , 0. ],
[0.25827815, 0.74172185]])
Mesures de performance¶
Accuracy¶
In [ ]:
# Sur la base test
Acc_test_AD = accuracy_score(Y_test, best_Y_test_pred_AD) * 100
Acc_train_AD = accuracy_score(Y_train, best_Y_train_pred_AD) * 100
# Sur la base train
print("Accuracy sur train:", Acc_train_AD, "%")
print("Accuracy sur test:", Acc_test_AD, "%")
Accuracy sur train: 89.84570168993388 % Accuracy sur test: 84.64175522797395 %
F1 score¶
In [ ]:
# Sur la base test et train
F1_score_test_AD = f1_score(Y_test, best_Y_test_pred_AD, average='weighted') * 100
F1_score_train_AD = f1_score(Y_train, best_Y_train_pred_AD, average='weighted') * 100
# Sur la base train
print("F1_score sur train:", F1_score_train_AD, "%")
print("F1_score sur test:", F1_score_test_AD, "%")
F1_score sur train: 89.77331870246697 % F1_score sur test: 84.54707200462465 %
Précision¶
In [ ]:
# Sur la base test et train
precision_score_test_AD = precision_score(Y_test, best_Y_test_pred_AD, average='weighted') * 100
precision_score_train_AD = precision_score(Y_train, best_Y_train_pred_AD, average='weighted') * 100
# Sur la base train
print("Précision sur train:", precision_score_train_AD, "%")
print("Précision sur test:", precision_score_test_AD, "%")
Précision sur train: 90.97259595247407 % Précision sur test: 85.58468624634192 %
Recall¶
In [ ]:
# Sur la base test et train
recall_score_test_AD = recall_score(Y_test, best_Y_test_pred_AD, average='weighted') * 100
recall_score_train_AD = recall_score(Y_train, best_Y_train_pred_AD, average='weighted') * 100
# Sur la base train
print("Recall sur train:", recall_score_train_AD, "%")
print("Recall sur test:", recall_score_test_AD, "%")
Recall sur train: 89.84570168993388 % Recall sur test: 84.64175522797395 %
Matrice de confusion¶
In [ ]:
matrice_AD = confusion_matrix(Y_test, best_Y_test_pred_AD)
# Affichage de la matrice de confusion avec titre
fig, ax = plot_confusion_matrix(conf_mat=matrice_AD,
show_absolute=True,
show_normed=True,
colorbar=True)
plt.title("Confusion Matrix AD")
plt.show()
print("Alors dans la classe 0,sur",Y_test.value_counts()[0],
"individu,le modèle réussit à faire un bon classement sur", matrice_AD[0,0],
" individu et une erreur sur", matrice_AD[0,1], "\n Dans la classe 1,sur",Y_test.value_counts()[1],
"individus le modèle fait un bon classement sur", matrice_AD[1,1],
" individu et une erreur sur", matrice_AD[0,0] )
Alors dans la classe 0,sur 1466 individu,le modèle réussit à faire un bon classement sur 1124 individu et une erreur sur 342 Dans la classe 1,sur 1451 individus le modèle fait un bon classement sur 1345 individu et une erreur sur 1124
*taux de bon et mauvais classement*
In [ ]:
print(f"Taux de bon classement (Classe 0 - Sans AVC): {matrice_AD[0, 0] / Y_test.value_counts()[0] * 100:.2f} %")
print(f"Taux de mauvais classement (Classe 0 - Sans AVC): {matrice_AD[0, 1] / Y_test.value_counts()[0] * 100:.2f} %")
print(f"Taux de bon classement (Classe 1 - Avec AVC): {matrice_AD[1, 1] / Y_test.value_counts()[1] * 100:.2f} %")
print(f"Taux de mauvais classement (Classe 1 - Avec AVC): {matrice_AD[1, 0] / Y_test.value_counts()[1] * 100:.2f} %")
Taux de bon classement (Classe 0 - Sans AVC): 76.67 % Taux de mauvais classement (Classe 0 - Sans AVC): 23.33 % Taux de bon classement (Classe 1 - Avec AVC): 92.69 % Taux de mauvais classement (Classe 1 - Avec AVC): 7.31 %
In [ ]:
# Courbe ROC
fpr_AD, tpr_AD, thresholds_AD = roc_curve(Y_test, best_modele_AD.predict_proba(X_test)[:, 1])
roc_auc_AD = auc(fpr_AD, tpr_AD)
plt.figure()
plt.plot(fpr_AD, tpr_AD, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc_AD)
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('Taux de faux positif')
plt.ylabel('Taux de vrai positif')
plt.title('Courbe de ROC pour Arbre de decision')
plt.legend(loc="lower right")
plt.show()
In [ ]:
result = permutation_importance(best_modele_AD, X, Y, n_repeats=10)
sorted_idx = result.importances_mean.argsort()
plt.barh(X.columns[sorted_idx], result.importances_mean[sorted_idx])
plt.xlabel("Importance des caractéristiques")
plt.show()
Random Forest¶
In [ ]:
modele = RandomForestClassifier()
# Définir le modèle et les paramètres
modele_RF = RandomForestClassifier()
param_grid = [{
'n_estimators':[1,2,3,4,5,6,7,8,9,10],
'max_depth':[1,2,3,4,5,6,7,8,9,10],
'min_samples_leaf':[1,2,3,4]
}
]
# Créer l'objet GridSearchCV
modele_opt_RF = GridSearchCV(modele_RF, # modèle initialisé
param_grid, # grilles de parametre du modèle
cv=5, # cross--validation
verbose=15 #longueur
)
##Entrainement du modèle
modele_opt_RF.fit(X_train,Y_train)
# parametre optimaux
best_param = modele_opt_RF.best_params_
print("VOILA LES MEILLEURS PARAMÉTRES :",best_param)
# meilleur modèle
best_modele_RF = modele_opt_RF.best_estimator_
best_modele_RF
Fitting 5 folds for each of 400 candidates, totalling 2000 fits
[CV 1/5; 1/400] START max_depth=1, min_samples_leaf=1, n_estimators=1...........
[CV 1/5; 1/400] END max_depth=1, min_samples_leaf=1, n_estimators=1;, score=0.608 total time= 0.0s
[CV 2/5; 1/400] START max_depth=1, min_samples_leaf=1, n_estimators=1...........
[CV 2/5; 1/400] END max_depth=1, min_samples_leaf=1, n_estimators=1;, score=0.788 total time= 0.0s
[CV 3/5; 1/400] START max_depth=1, min_samples_leaf=1, n_estimators=1...........
[CV 3/5; 1/400] END max_depth=1, min_samples_leaf=1, n_estimators=1;, score=0.602 total time= 0.0s
[CV 4/5; 1/400] START max_depth=1, min_samples_leaf=1, n_estimators=1...........
[CV 4/5; 1/400] END max_depth=1, min_samples_leaf=1, n_estimators=1;, score=0.790 total time= 0.0s
[CV 5/5; 1/400] START max_depth=1, min_samples_leaf=1, n_estimators=1...........
[CV 5/5; 1/400] END max_depth=1, min_samples_leaf=1, n_estimators=1;, score=0.557 total time= 0.0s
[CV 1/5; 2/400] START max_depth=1, min_samples_leaf=1, n_estimators=2...........
[CV 1/5; 2/400] END max_depth=1, min_samples_leaf=1, n_estimators=2;, score=0.741 total time= 0.0s
[CV 2/5; 2/400] START max_depth=1, min_samples_leaf=1, n_estimators=2...........
[CV 2/5; 2/400] END max_depth=1, min_samples_leaf=1, n_estimators=2;, score=0.788 total time= 0.0s
[CV 3/5; 2/400] START max_depth=1, min_samples_leaf=1, n_estimators=2...........
[CV 3/5; 2/400] END max_depth=1, min_samples_leaf=1, n_estimators=2;, score=0.780 total time= 0.0s
[CV 4/5; 2/400] START max_depth=1, min_samples_leaf=1, n_estimators=2...........
[CV 4/5; 2/400] END max_depth=1, min_samples_leaf=1, n_estimators=2;, score=0.571 total time= 0.0s
[CV 5/5; 2/400] START max_depth=1, min_samples_leaf=1, n_estimators=2...........
[CV 5/5; 2/400] END max_depth=1, min_samples_leaf=1, n_estimators=2;, score=0.557 total time= 0.0s
[CV 1/5; 3/400] START max_depth=1, min_samples_leaf=1, n_estimators=3...........
[CV 1/5; 3/400] END max_depth=1, min_samples_leaf=1, n_estimators=3;, score=0.787 total time= 0.0s
[CV 2/5; 3/400] START max_depth=1, min_samples_leaf=1, n_estimators=3...........
[CV 2/5; 3/400] END max_depth=1, min_samples_leaf=1, n_estimators=3;, score=0.788 total time= 0.0s
[CV 3/5; 3/400] START max_depth=1, min_samples_leaf=1, n_estimators=3...........
[CV 3/5; 3/400] END max_depth=1, min_samples_leaf=1, n_estimators=3;, score=0.780 total time= 0.0s
[CV 4/5; 3/400] START max_depth=1, min_samples_leaf=1, n_estimators=3...........
[CV 4/5; 3/400] END max_depth=1, min_samples_leaf=1, n_estimators=3;, score=0.788 total time= 0.0s
[CV 5/5; 3/400] START max_depth=1, min_samples_leaf=1, n_estimators=3...........
[CV 5/5; 3/400] END max_depth=1, min_samples_leaf=1, n_estimators=3;, score=0.770 total time= 0.0s
[CV 1/5; 4/400] START max_depth=1, min_samples_leaf=1, n_estimators=4...........
[CV 1/5; 4/400] END max_depth=1, min_samples_leaf=1, n_estimators=4;, score=0.796 total time= 0.0s
[CV 2/5; 4/400] START max_depth=1, min_samples_leaf=1, n_estimators=4...........
[CV 2/5; 4/400] END max_depth=1, min_samples_leaf=1, n_estimators=4;, score=0.788 total time= 0.0s
[CV 3/5; 4/400] START max_depth=1, min_samples_leaf=1, n_estimators=4...........
[CV 3/5; 4/400] END max_depth=1, min_samples_leaf=1, n_estimators=4;, score=0.738 total time= 0.0s
[CV 4/5; 4/400] START max_depth=1, min_samples_leaf=1, n_estimators=4...........
[CV 4/5; 4/400] END max_depth=1, min_samples_leaf=1, n_estimators=4;, score=0.789 total time= 0.0s
[CV 5/5; 4/400] START max_depth=1, min_samples_leaf=1, n_estimators=4...........
[CV 5/5; 4/400] END max_depth=1, min_samples_leaf=1, n_estimators=4;, score=0.778 total time= 0.0s
[CV 1/5; 5/400] START max_depth=1, min_samples_leaf=1, n_estimators=5...........
[CV 1/5; 5/400] END max_depth=1, min_samples_leaf=1, n_estimators=5;, score=0.711 total time= 0.0s
[CV 2/5; 5/400] START max_depth=1, min_samples_leaf=1, n_estimators=5...........
[CV 2/5; 5/400] END max_depth=1, min_samples_leaf=1, n_estimators=5;, score=0.788 total time= 0.0s
[CV 3/5; 5/400] START max_depth=1, min_samples_leaf=1, n_estimators=5...........
[CV 3/5; 5/400] END max_depth=1, min_samples_leaf=1, n_estimators=5;, score=0.745 total time= 0.0s
[CV 4/5; 5/400] START max_depth=1, min_samples_leaf=1, n_estimators=5...........
[CV 4/5; 5/400] END max_depth=1, min_samples_leaf=1, n_estimators=5;, score=0.780 total time= 0.0s
[CV 5/5; 5/400] START max_depth=1, min_samples_leaf=1, n_estimators=5...........
[CV 5/5; 5/400] END max_depth=1, min_samples_leaf=1, n_estimators=5;, score=0.721 total time= 0.0s
[CV 1/5; 6/400] START max_depth=1, min_samples_leaf=1, n_estimators=6...........
[CV 1/5; 6/400] END max_depth=1, min_samples_leaf=1, n_estimators=6;, score=0.755 total time= 0.0s
[CV 2/5; 6/400] START max_depth=1, min_samples_leaf=1, n_estimators=6...........
[CV 2/5; 6/400] END max_depth=1, min_samples_leaf=1, n_estimators=6;, score=0.752 total time= 0.0s
[CV 3/5; 6/400] START max_depth=1, min_samples_leaf=1, n_estimators=6...........
[CV 3/5; 6/400] END max_depth=1, min_samples_leaf=1, n_estimators=6;, score=0.769 total time= 0.0s
[CV 4/5; 6/400] START max_depth=1, min_samples_leaf=1, n_estimators=6...........
[CV 4/5; 6/400] END max_depth=1, min_samples_leaf=1, n_estimators=6;, score=0.790 total time= 0.0s
[CV 5/5; 6/400] START max_depth=1, min_samples_leaf=1, n_estimators=6...........
[CV 5/5; 6/400] END max_depth=1, min_samples_leaf=1, n_estimators=6;, score=0.769 total time= 0.0s
[CV 1/5; 7/400] START max_depth=1, min_samples_leaf=1, n_estimators=7...........
[CV 1/5; 7/400] END max_depth=1, min_samples_leaf=1, n_estimators=7;, score=0.760 total time= 0.0s
[CV 2/5; 7/400] START max_depth=1, min_samples_leaf=1, n_estimators=7...........
[CV 2/5; 7/400] END max_depth=1, min_samples_leaf=1, n_estimators=7;, score=0.788 total time= 0.0s
[CV 3/5; 7/400] START max_depth=1, min_samples_leaf=1, n_estimators=7...........
[CV 3/5; 7/400] END max_depth=1, min_samples_leaf=1, n_estimators=7;, score=0.778 total time= 0.0s
[CV 4/5; 7/400] START max_depth=1, min_samples_leaf=1, n_estimators=7...........
[CV 4/5; 7/400] END max_depth=1, min_samples_leaf=1, n_estimators=7;, score=0.769 total time= 0.0s
[CV 5/5; 7/400] START max_depth=1, min_samples_leaf=1, n_estimators=7...........
[CV 5/5; 7/400] END max_depth=1, min_samples_leaf=1, n_estimators=7;, score=0.777 total time= 0.0s
[CV 1/5; 8/400] START max_depth=1, min_samples_leaf=1, n_estimators=8...........
[CV 1/5; 8/400] END max_depth=1, min_samples_leaf=1, n_estimators=8;, score=0.782 total time= 0.0s
[CV 2/5; 8/400] START max_depth=1, min_samples_leaf=1, n_estimators=8...........
[CV 2/5; 8/400] END max_depth=1, min_samples_leaf=1, n_estimators=8;, score=0.788 total time= 0.0s
[CV 3/5; 8/400] START max_depth=1, min_samples_leaf=1, n_estimators=8...........
[CV 3/5; 8/400] END max_depth=1, min_samples_leaf=1, n_estimators=8;, score=0.777 total time= 0.0s
[CV 4/5; 8/400] START max_depth=1, min_samples_leaf=1, n_estimators=8...........
[CV 4/5; 8/400] END max_depth=1, min_samples_leaf=1, n_estimators=8;, score=0.788 total time= 0.0s
[CV 5/5; 8/400] START max_depth=1, min_samples_leaf=1, n_estimators=8...........
[CV 5/5; 8/400] END max_depth=1, min_samples_leaf=1, n_estimators=8;, score=0.724 total time= 0.0s
[CV 1/5; 9/400] START max_depth=1, min_samples_leaf=1, n_estimators=9...........
[CV 1/5; 9/400] END max_depth=1, min_samples_leaf=1, n_estimators=9;, score=0.766 total time= 0.0s
[CV 2/5; 9/400] START max_depth=1, min_samples_leaf=1, n_estimators=9...........
[CV 2/5; 9/400] END max_depth=1, min_samples_leaf=1, n_estimators=9;, score=0.788 total time= 0.0s
[CV 3/5; 9/400] START max_depth=1, min_samples_leaf=1, n_estimators=9...........
[CV 3/5; 9/400] END max_depth=1, min_samples_leaf=1, n_estimators=9;, score=0.774 total time= 0.0s
[CV 4/5; 9/400] START max_depth=1, min_samples_leaf=1, n_estimators=9...........
[CV 4/5; 9/400] END max_depth=1, min_samples_leaf=1, n_estimators=9;, score=0.730 total time= 0.0s
[CV 5/5; 9/400] START max_depth=1, min_samples_leaf=1, n_estimators=9...........
[CV 5/5; 9/400] END max_depth=1, min_samples_leaf=1, n_estimators=9;, score=0.769 total time= 0.0s
[CV 1/5; 10/400] START max_depth=1, min_samples_leaf=1, n_estimators=10.........
[CV 1/5; 10/400] END max_depth=1, min_samples_leaf=1, n_estimators=10;, score=0.756 total time= 0.1s
[CV 2/5; 10/400] START max_depth=1, min_samples_leaf=1, n_estimators=10.........
[CV 2/5; 10/400] END max_depth=1, min_samples_leaf=1, n_estimators=10;, score=0.776 total time= 0.0s
[CV 3/5; 10/400] START max_depth=1, min_samples_leaf=1, n_estimators=10.........
[CV 3/5; 10/400] END max_depth=1, min_samples_leaf=1, n_estimators=10;, score=0.779 total time= 0.0s
[CV 4/5; 10/400] START max_depth=1, min_samples_leaf=1, n_estimators=10.........
[CV 4/5; 10/400] END max_depth=1, min_samples_leaf=1, n_estimators=10;, score=0.790 total time= 0.0s
[CV 5/5; 10/400] START max_depth=1, min_samples_leaf=1, n_estimators=10.........
[CV 5/5; 10/400] END max_depth=1, min_samples_leaf=1, n_estimators=10;, score=0.758 total time= 0.0s
[CV 1/5; 11/400] START max_depth=1, min_samples_leaf=2, n_estimators=1..........
[CV 1/5; 11/400] END max_depth=1, min_samples_leaf=2, n_estimators=1;, score=0.608 total time= 0.0s
[CV 2/5; 11/400] START max_depth=1, min_samples_leaf=2, n_estimators=1..........
[CV 2/5; 11/400] END max_depth=1, min_samples_leaf=2, n_estimators=1;, score=0.572 total time= 0.0s
[CV 3/5; 11/400] START max_depth=1, min_samples_leaf=2, n_estimators=1..........
[CV 3/5; 11/400] END max_depth=1, min_samples_leaf=2, n_estimators=1;, score=0.780 total time= 0.0s
[CV 4/5; 11/400] START max_depth=1, min_samples_leaf=2, n_estimators=1..........
[CV 4/5; 11/400] END max_depth=1, min_samples_leaf=2, n_estimators=1;, score=0.624 total time= 0.0s
[CV 5/5; 11/400] START max_depth=1, min_samples_leaf=2, n_estimators=1..........
[CV 5/5; 11/400] END max_depth=1, min_samples_leaf=2, n_estimators=1;, score=0.777 total time= 0.0s
[CV 1/5; 12/400] START max_depth=1, min_samples_leaf=2, n_estimators=2..........
[CV 1/5; 12/400] END max_depth=1, min_samples_leaf=2, n_estimators=2;, score=0.787 total time= 0.0s
[CV 2/5; 12/400] START max_depth=1, min_samples_leaf=2, n_estimators=2..........
[CV 2/5; 12/400] END max_depth=1, min_samples_leaf=2, n_estimators=2;, score=0.788 total time= 0.0s
[CV 3/5; 12/400] START max_depth=1, min_samples_leaf=2, n_estimators=2..........
[CV 3/5; 12/400] END max_depth=1, min_samples_leaf=2, n_estimators=2;, score=0.621 total time= 0.0s
[CV 4/5; 12/400] START max_depth=1, min_samples_leaf=2, n_estimators=2..........
[CV 4/5; 12/400] END max_depth=1, min_samples_leaf=2, n_estimators=2;, score=0.790 total time= 0.0s
[CV 5/5; 12/400] START max_depth=1, min_samples_leaf=2, n_estimators=2..........
[CV 5/5; 12/400] END max_depth=1, min_samples_leaf=2, n_estimators=2;, score=0.597 total time= 0.0s
[CV 1/5; 13/400] START max_depth=1, min_samples_leaf=2, n_estimators=3..........
[CV 1/5; 13/400] END max_depth=1, min_samples_leaf=2, n_estimators=3;, score=0.743 total time= 0.0s
[CV 2/5; 13/400] START max_depth=1, min_samples_leaf=2, n_estimators=3..........
[CV 2/5; 13/400] END max_depth=1, min_samples_leaf=2, n_estimators=3;, score=0.785 total time= 0.0s
[CV 3/5; 13/400] START max_depth=1, min_samples_leaf=2, n_estimators=3..........
[CV 3/5; 13/400] END max_depth=1, min_samples_leaf=2, n_estimators=3;, score=0.589 total time= 0.0s
[CV 4/5; 13/400] START max_depth=1, min_samples_leaf=2, n_estimators=3..........
[CV 4/5; 13/400] END max_depth=1, min_samples_leaf=2, n_estimators=3;, score=0.774 total time= 0.0s
[CV 5/5; 13/400] START max_depth=1, min_samples_leaf=2, n_estimators=3..........
[CV 5/5; 13/400] END max_depth=1, min_samples_leaf=2, n_estimators=3;, score=0.769 total time= 0.0s
[CV 1/5; 14/400] START max_depth=1, min_samples_leaf=2, n_estimators=4..........
[CV 1/5; 14/400] END max_depth=1, min_samples_leaf=2, n_estimators=4;, score=0.790 total time= 0.0s
[CV 2/5; 14/400] START max_depth=1, min_samples_leaf=2, n_estimators=4..........
[CV 2/5; 14/400] END max_depth=1, min_samples_leaf=2, n_estimators=4;, score=0.785 total time= 0.0s
[CV 3/5; 14/400] START max_depth=1, min_samples_leaf=2, n_estimators=4..........
[CV 3/5; 14/400] END max_depth=1, min_samples_leaf=2, n_estimators=4;, score=0.767 total time= 0.0s
[CV 4/5; 14/400] START max_depth=1, min_samples_leaf=2, n_estimators=4..........
[CV 4/5; 14/400] END max_depth=1, min_samples_leaf=2, n_estimators=4;, score=0.790 total time= 0.0s
[CV 5/5; 14/400] START max_depth=1, min_samples_leaf=2, n_estimators=4..........
[CV 5/5; 14/400] END max_depth=1, min_samples_leaf=2, n_estimators=4;, score=0.771 total time= 0.0s
[CV 1/5; 15/400] START max_depth=1, min_samples_leaf=2, n_estimators=5..........
[CV 1/5; 15/400] END max_depth=1, min_samples_leaf=2, n_estimators=5;, score=0.791 total time= 0.0s
[CV 2/5; 15/400] START max_depth=1, min_samples_leaf=2, n_estimators=5..........
[CV 2/5; 15/400] END max_depth=1, min_samples_leaf=2, n_estimators=5;, score=0.788 total time= 0.0s
[CV 3/5; 15/400] START max_depth=1, min_samples_leaf=2, n_estimators=5..........
[CV 3/5; 15/400] END max_depth=1, min_samples_leaf=2, n_estimators=5;, score=0.769 total time= 0.0s
[CV 4/5; 15/400] START max_depth=1, min_samples_leaf=2, n_estimators=5..........
[CV 4/5; 15/400] END max_depth=1, min_samples_leaf=2, n_estimators=5;, score=0.788 total time= 0.0s
[CV 5/5; 15/400] START max_depth=1, min_samples_leaf=2, n_estimators=5..........
[CV 5/5; 15/400] END max_depth=1, min_samples_leaf=2, n_estimators=5;, score=0.777 total time= 0.0s
[CV 1/5; 16/400] START max_depth=1, min_samples_leaf=2, n_estimators=6..........
[CV 1/5; 16/400] END max_depth=1, min_samples_leaf=2, n_estimators=6;, score=0.790 total time= 0.0s
[CV 2/5; 16/400] START max_depth=1, min_samples_leaf=2, n_estimators=6..........
[CV 2/5; 16/400] END max_depth=1, min_samples_leaf=2, n_estimators=6;, score=0.788 total time= 0.0s
[CV 3/5; 16/400] START max_depth=1, min_samples_leaf=2, n_estimators=6..........
[CV 3/5; 16/400] END max_depth=1, min_samples_leaf=2, n_estimators=6;, score=0.780 total time= 0.0s
[CV 4/5; 16/400] START max_depth=1, min_samples_leaf=2, n_estimators=6..........
[CV 4/5; 16/400] END max_depth=1, min_samples_leaf=2, n_estimators=6;, score=0.780 total time= 0.0s
[CV 5/5; 16/400] START max_depth=1, min_samples_leaf=2, n_estimators=6..........
[CV 5/5; 16/400] END max_depth=1, min_samples_leaf=2, n_estimators=6;, score=0.769 total time= 0.0s
[CV 1/5; 17/400] START max_depth=1, min_samples_leaf=2, n_estimators=7..........
[CV 1/5; 17/400] END max_depth=1, min_samples_leaf=2, n_estimators=7;, score=0.791 total time= 0.0s
[CV 2/5; 17/400] START max_depth=1, min_samples_leaf=2, n_estimators=7..........
[CV 2/5; 17/400] END max_depth=1, min_samples_leaf=2, n_estimators=7;, score=0.788 total time= 0.0s
[CV 3/5; 17/400] START max_depth=1, min_samples_leaf=2, n_estimators=7..........
[CV 3/5; 17/400] END max_depth=1, min_samples_leaf=2, n_estimators=7;, score=0.780 total time= 0.0s
[CV 4/5; 17/400] START max_depth=1, min_samples_leaf=2, n_estimators=7..........
[CV 4/5; 17/400] END max_depth=1, min_samples_leaf=2, n_estimators=7;, score=0.757 total time= 0.0s
[CV 5/5; 17/400] START max_depth=1, min_samples_leaf=2, n_estimators=7..........
[CV 5/5; 17/400] END max_depth=1, min_samples_leaf=2, n_estimators=7;, score=0.769 total time= 0.0s
[CV 1/5; 18/400] START max_depth=1, min_samples_leaf=2, n_estimators=8..........
[CV 1/5; 18/400] END max_depth=1, min_samples_leaf=2, n_estimators=8;, score=0.782 total time= 0.0s
[CV 2/5; 18/400] START max_depth=1, min_samples_leaf=2, n_estimators=8..........
[CV 2/5; 18/400] END max_depth=1, min_samples_leaf=2, n_estimators=8;, score=0.769 total time= 0.0s
[CV 3/5; 18/400] START max_depth=1, min_samples_leaf=2, n_estimators=8..........
[CV 3/5; 18/400] END max_depth=1, min_samples_leaf=2, n_estimators=8;, score=0.764 total time= 0.1s
[CV 4/5; 18/400] START max_depth=1, min_samples_leaf=2, n_estimators=8..........
[CV 4/5; 18/400] END max_depth=1, min_samples_leaf=2, n_estimators=8;, score=0.788 total time= 0.0s
[CV 5/5; 18/400] START max_depth=1, min_samples_leaf=2, n_estimators=8..........
[CV 5/5; 18/400] END max_depth=1, min_samples_leaf=2, n_estimators=8;, score=0.774 total time= 0.0s
[CV 1/5; 19/400] START max_depth=1, min_samples_leaf=2, n_estimators=9..........
[CV 1/5; 19/400] END max_depth=1, min_samples_leaf=2, n_estimators=9;, score=0.796 total time= 0.0s
[CV 2/5; 19/400] START max_depth=1, min_samples_leaf=2, n_estimators=9..........
[CV 2/5; 19/400] END max_depth=1, min_samples_leaf=2, n_estimators=9;, score=0.669 total time= 0.0s
[CV 3/5; 19/400] START max_depth=1, min_samples_leaf=2, n_estimators=9..........
[CV 3/5; 19/400] END max_depth=1, min_samples_leaf=2, n_estimators=9;, score=0.780 total time= 0.0s
[CV 4/5; 19/400] START max_depth=1, min_samples_leaf=2, n_estimators=9..........
[CV 4/5; 19/400] END max_depth=1, min_samples_leaf=2, n_estimators=9;, score=0.790 total time= 0.0s
[CV 5/5; 19/400] START max_depth=1, min_samples_leaf=2, n_estimators=9..........
[CV 5/5; 19/400] END max_depth=1, min_samples_leaf=2, n_estimators=9;, score=0.713 total time= 0.0s
[CV 1/5; 20/400] START max_depth=1, min_samples_leaf=2, n_estimators=10.........
[CV 1/5; 20/400] END max_depth=1, min_samples_leaf=2, n_estimators=10;, score=0.777 total time= 0.0s
[CV 2/5; 20/400] START max_depth=1, min_samples_leaf=2, n_estimators=10.........
[CV 2/5; 20/400] END max_depth=1, min_samples_leaf=2, n_estimators=10;, score=0.788 total time= 0.0s
[CV 3/5; 20/400] START max_depth=1, min_samples_leaf=2, n_estimators=10.........
[CV 3/5; 20/400] END max_depth=1, min_samples_leaf=2, n_estimators=10;, score=0.780 total time= 0.0s
[CV 4/5; 20/400] START max_depth=1, min_samples_leaf=2, n_estimators=10.........
[CV 4/5; 20/400] END max_depth=1, min_samples_leaf=2, n_estimators=10;, score=0.785 total time= 0.0s
[CV 5/5; 20/400] START max_depth=1, min_samples_leaf=2, n_estimators=10.........
[CV 5/5; 20/400] END max_depth=1, min_samples_leaf=2, n_estimators=10;, score=0.769 total time= 0.0s
[CV 1/5; 21/400] START max_depth=1, min_samples_leaf=3, n_estimators=1..........
[CV 1/5; 21/400] END max_depth=1, min_samples_leaf=3, n_estimators=1;, score=0.592 total time= 0.0s
[CV 2/5; 21/400] START max_depth=1, min_samples_leaf=3, n_estimators=1..........
[CV 2/5; 21/400] END max_depth=1, min_samples_leaf=3, n_estimators=1;, score=0.630 total time= 0.0s
[CV 3/5; 21/400] START max_depth=1, min_samples_leaf=3, n_estimators=1..........
[CV 3/5; 21/400] END max_depth=1, min_samples_leaf=3, n_estimators=1;, score=0.570 total time= 0.0s
[CV 4/5; 21/400] START max_depth=1, min_samples_leaf=3, n_estimators=1..........
[CV 4/5; 21/400] END max_depth=1, min_samples_leaf=3, n_estimators=1;, score=0.609 total time= 0.0s
[CV 5/5; 21/400] START max_depth=1, min_samples_leaf=3, n_estimators=1..........
[CV 5/5; 21/400] END max_depth=1, min_samples_leaf=3, n_estimators=1;, score=0.622 total time= 0.0s
[CV 1/5; 22/400] START max_depth=1, min_samples_leaf=3, n_estimators=2..........
[CV 1/5; 22/400] END max_depth=1, min_samples_leaf=3, n_estimators=2;, score=0.774 total time= 0.0s
[CV 2/5; 22/400] START max_depth=1, min_samples_leaf=3, n_estimators=2..........
[CV 2/5; 22/400] END max_depth=1, min_samples_leaf=3, n_estimators=2;, score=0.788 total time= 0.0s
[CV 3/5; 22/400] START max_depth=1, min_samples_leaf=3, n_estimators=2..........
[CV 3/5; 22/400] END max_depth=1, min_samples_leaf=3, n_estimators=2;, score=0.780 total time= 0.0s
[CV 4/5; 22/400] START max_depth=1, min_samples_leaf=3, n_estimators=2..........
[CV 4/5; 22/400] END max_depth=1, min_samples_leaf=3, n_estimators=2;, score=0.790 total time= 0.0s
[CV 5/5; 22/400] START max_depth=1, min_samples_leaf=3, n_estimators=2..........
[CV 5/5; 22/400] END max_depth=1, min_samples_leaf=3, n_estimators=2;, score=0.763 total time= 0.0s
[CV 1/5; 23/400] START max_depth=1, min_samples_leaf=3, n_estimators=3..........
[CV 1/5; 23/400] END max_depth=1, min_samples_leaf=3, n_estimators=3;, score=0.592 total time= 0.0s
[CV 2/5; 23/400] START max_depth=1, min_samples_leaf=3, n_estimators=3..........
[CV 2/5; 23/400] END max_depth=1, min_samples_leaf=3, n_estimators=3;, score=0.788 total time= 0.0s
[CV 3/5; 23/400] START max_depth=1, min_samples_leaf=3, n_estimators=3..........
[CV 3/5; 23/400] END max_depth=1, min_samples_leaf=3, n_estimators=3;, score=0.766 total time= 0.0s
[CV 4/5; 23/400] START max_depth=1, min_samples_leaf=3, n_estimators=3..........
[CV 4/5; 23/400] END max_depth=1, min_samples_leaf=3, n_estimators=3;, score=0.790 total time= 0.0s
[CV 5/5; 23/400] START max_depth=1, min_samples_leaf=3, n_estimators=3..........
[CV 5/5; 23/400] END max_depth=1, min_samples_leaf=3, n_estimators=3;, score=0.551 total time= 0.0s
[CV 1/5; 24/400] START max_depth=1, min_samples_leaf=3, n_estimators=4..........
[CV 1/5; 24/400] END max_depth=1, min_samples_leaf=3, n_estimators=4;, score=0.617 total time= 0.0s
[CV 2/5; 24/400] START max_depth=1, min_samples_leaf=3, n_estimators=4..........
[CV 2/5; 24/400] END max_depth=1, min_samples_leaf=3, n_estimators=4;, score=0.611 total time= 0.0s
[CV 3/5; 24/400] START max_depth=1, min_samples_leaf=3, n_estimators=4..........
[CV 3/5; 24/400] END max_depth=1, min_samples_leaf=3, n_estimators=4;, score=0.778 total time= 0.0s
[CV 4/5; 24/400] START max_depth=1, min_samples_leaf=3, n_estimators=4..........
[CV 4/5; 24/400] END max_depth=1, min_samples_leaf=3, n_estimators=4;, score=0.790 total time= 0.0s
[CV 5/5; 24/400] START max_depth=1, min_samples_leaf=3, n_estimators=4..........
[CV 5/5; 24/400] END max_depth=1, min_samples_leaf=3, n_estimators=4;, score=0.717 total time= 0.0s
[CV 1/5; 25/400] START max_depth=1, min_samples_leaf=3, n_estimators=5..........
[CV 1/5; 25/400] END max_depth=1, min_samples_leaf=3, n_estimators=5;, score=0.746 total time= 0.0s
[CV 2/5; 25/400] START max_depth=1, min_samples_leaf=3, n_estimators=5..........
[CV 2/5; 25/400] END max_depth=1, min_samples_leaf=3, n_estimators=5;, score=0.754 total time= 0.0s
[CV 3/5; 25/400] START max_depth=1, min_samples_leaf=3, n_estimators=5..........
[CV 3/5; 25/400] END max_depth=1, min_samples_leaf=3, n_estimators=5;, score=0.780 total time= 0.0s
[CV 4/5; 25/400] START max_depth=1, min_samples_leaf=3, n_estimators=5..........
[CV 4/5; 25/400] END max_depth=1, min_samples_leaf=3, n_estimators=5;, score=0.790 total time= 0.0s
[CV 5/5; 25/400] START max_depth=1, min_samples_leaf=3, n_estimators=5..........
[CV 5/5; 25/400] END max_depth=1, min_samples_leaf=3, n_estimators=5;, score=0.777 total time= 0.0s
[CV 1/5; 26/400] START max_depth=1, min_samples_leaf=3, n_estimators=6..........
[CV 1/5; 26/400] END max_depth=1, min_samples_leaf=3, n_estimators=6;, score=0.783 total time= 0.0s
[CV 2/5; 26/400] START max_depth=1, min_samples_leaf=3, n_estimators=6..........
[CV 2/5; 26/400] END max_depth=1, min_samples_leaf=3, n_estimators=6;, score=0.778 total time= 0.0s
[CV 3/5; 26/400] START max_depth=1, min_samples_leaf=3, n_estimators=6..........
[CV 3/5; 26/400] END max_depth=1, min_samples_leaf=3, n_estimators=6;, score=0.779 total time= 0.0s
[CV 4/5; 26/400] START max_depth=1, min_samples_leaf=3, n_estimators=6..........
[CV 4/5; 26/400] END max_depth=1, min_samples_leaf=3, n_estimators=6;, score=0.736 total time= 0.0s
[CV 5/5; 26/400] START max_depth=1, min_samples_leaf=3, n_estimators=6..........
[CV 5/5; 26/400] END max_depth=1, min_samples_leaf=3, n_estimators=6;, score=0.777 total time= 0.0s
[CV 1/5; 27/400] START max_depth=1, min_samples_leaf=3, n_estimators=7..........
[CV 1/5; 27/400] END max_depth=1, min_samples_leaf=3, n_estimators=7;, score=0.787 total time= 0.0s
[CV 2/5; 27/400] START max_depth=1, min_samples_leaf=3, n_estimators=7..........
[CV 2/5; 27/400] END max_depth=1, min_samples_leaf=3, n_estimators=7;, score=0.633 total time= 0.0s
[CV 3/5; 27/400] START max_depth=1, min_samples_leaf=3, n_estimators=7..........
[CV 3/5; 27/400] END max_depth=1, min_samples_leaf=3, n_estimators=7;, score=0.760 total time= 0.0s
[CV 4/5; 27/400] START max_depth=1, min_samples_leaf=3, n_estimators=7..........
[CV 4/5; 27/400] END max_depth=1, min_samples_leaf=3, n_estimators=7;, score=0.774 total time= 0.0s
[CV 5/5; 27/400] START max_depth=1, min_samples_leaf=3, n_estimators=7..........
[CV 5/5; 27/400] END max_depth=1, min_samples_leaf=3, n_estimators=7;, score=0.767 total time= 0.0s
[CV 1/5; 28/400] START max_depth=1, min_samples_leaf=3, n_estimators=8..........
[CV 1/5; 28/400] END max_depth=1, min_samples_leaf=3, n_estimators=8;, score=0.787 total time= 0.0s
[CV 2/5; 28/400] START max_depth=1, min_samples_leaf=3, n_estimators=8..........
[CV 2/5; 28/400] END max_depth=1, min_samples_leaf=3, n_estimators=8;, score=0.788 total time= 0.0s
[CV 3/5; 28/400] START max_depth=1, min_samples_leaf=3, n_estimators=8..........
[CV 3/5; 28/400] END max_depth=1, min_samples_leaf=3, n_estimators=8;, score=0.767 total time= 0.0s
[CV 4/5; 28/400] START max_depth=1, min_samples_leaf=3, n_estimators=8..........
[CV 4/5; 28/400] END max_depth=1, min_samples_leaf=3, n_estimators=8;, score=0.755 total time= 0.0s
[CV 5/5; 28/400] START max_depth=1, min_samples_leaf=3, n_estimators=8..........
[CV 5/5; 28/400] END max_depth=1, min_samples_leaf=3, n_estimators=8;, score=0.774 total time= 0.0s
[CV 1/5; 29/400] START max_depth=1, min_samples_leaf=3, n_estimators=9..........
[CV 1/5; 29/400] END max_depth=1, min_samples_leaf=3, n_estimators=9;, score=0.787 total time= 0.0s
[CV 2/5; 29/400] START max_depth=1, min_samples_leaf=3, n_estimators=9..........
[CV 2/5; 29/400] END max_depth=1, min_samples_leaf=3, n_estimators=9;, score=0.788 total time= 0.0s
[CV 3/5; 29/400] START max_depth=1, min_samples_leaf=3, n_estimators=9..........
[CV 3/5; 29/400] END max_depth=1, min_samples_leaf=3, n_estimators=9;, score=0.780 total time= 0.0s
[CV 4/5; 29/400] START max_depth=1, min_samples_leaf=3, n_estimators=9..........
[CV 4/5; 29/400] END max_depth=1, min_samples_leaf=3, n_estimators=9;, score=0.790 total time= 0.0s
[CV 5/5; 29/400] START max_depth=1, min_samples_leaf=3, n_estimators=9..........
[CV 5/5; 29/400] END max_depth=1, min_samples_leaf=3, n_estimators=9;, score=0.721 total time= 0.0s
[CV 1/5; 30/400] START max_depth=1, min_samples_leaf=3, n_estimators=10.........
[CV 1/5; 30/400] END max_depth=1, min_samples_leaf=3, n_estimators=10;, score=0.792 total time= 0.0s
[CV 2/5; 30/400] START max_depth=1, min_samples_leaf=3, n_estimators=10.........
[CV 2/5; 30/400] END max_depth=1, min_samples_leaf=3, n_estimators=10;, score=0.643 total time= 0.0s
[CV 3/5; 30/400] START max_depth=1, min_samples_leaf=3, n_estimators=10.........
[CV 3/5; 30/400] END max_depth=1, min_samples_leaf=3, n_estimators=10;, score=0.780 total time= 0.0s
[CV 4/5; 30/400] START max_depth=1, min_samples_leaf=3, n_estimators=10.........
[CV 4/5; 30/400] END max_depth=1, min_samples_leaf=3, n_estimators=10;, score=0.790 total time= 0.0s
[CV 5/5; 30/400] START max_depth=1, min_samples_leaf=3, n_estimators=10.........
[CV 5/5; 30/400] END max_depth=1, min_samples_leaf=3, n_estimators=10;, score=0.707 total time= 0.0s
[CV 1/5; 31/400] START max_depth=1, min_samples_leaf=4, n_estimators=1..........
[CV 1/5; 31/400] END max_depth=1, min_samples_leaf=4, n_estimators=1;, score=0.533 total time= 0.0s
[CV 2/5; 31/400] START max_depth=1, min_samples_leaf=4, n_estimators=1..........
[CV 2/5; 31/400] END max_depth=1, min_samples_leaf=4, n_estimators=1;, score=0.788 total time= 0.0s
[CV 3/5; 31/400] START max_depth=1, min_samples_leaf=4, n_estimators=1..........
[CV 3/5; 31/400] END max_depth=1, min_samples_leaf=4, n_estimators=1;, score=0.602 total time= 0.0s
[CV 4/5; 31/400] START max_depth=1, min_samples_leaf=4, n_estimators=1..........
[CV 4/5; 31/400] END max_depth=1, min_samples_leaf=4, n_estimators=1;, score=0.571 total time= 0.0s
[CV 5/5; 31/400] START max_depth=1, min_samples_leaf=4, n_estimators=1..........
[CV 5/5; 31/400] END max_depth=1, min_samples_leaf=4, n_estimators=1;, score=0.769 total time= 0.0s
[CV 1/5; 32/400] START max_depth=1, min_samples_leaf=4, n_estimators=2..........
[CV 1/5; 32/400] END max_depth=1, min_samples_leaf=4, n_estimators=2;, score=0.796 total time= 0.0s
[CV 2/5; 32/400] START max_depth=1, min_samples_leaf=4, n_estimators=2..........
[CV 2/5; 32/400] END max_depth=1, min_samples_leaf=4, n_estimators=2;, score=0.630 total time= 0.0s
[CV 3/5; 32/400] START max_depth=1, min_samples_leaf=4, n_estimators=2..........
[CV 3/5; 32/400] END max_depth=1, min_samples_leaf=4, n_estimators=2;, score=0.780 total time= 0.0s
[CV 4/5; 32/400] START max_depth=1, min_samples_leaf=4, n_estimators=2..........
[CV 4/5; 32/400] END max_depth=1, min_samples_leaf=4, n_estimators=2;, score=0.790 total time= 0.0s
[CV 5/5; 32/400] START max_depth=1, min_samples_leaf=4, n_estimators=2..........
[CV 5/5; 32/400] END max_depth=1, min_samples_leaf=4, n_estimators=2;, score=0.769 total time= 0.0s
[CV 1/5; 33/400] START max_depth=1, min_samples_leaf=4, n_estimators=3..........
[CV 1/5; 33/400] END max_depth=1, min_samples_leaf=4, n_estimators=3;, score=0.608 total time= 0.0s
[CV 2/5; 33/400] START max_depth=1, min_samples_leaf=4, n_estimators=3..........
[CV 2/5; 33/400] END max_depth=1, min_samples_leaf=4, n_estimators=3;, score=0.622 total time= 0.0s
[CV 3/5; 33/400] START max_depth=1, min_samples_leaf=4, n_estimators=3..........
[CV 3/5; 33/400] END max_depth=1, min_samples_leaf=4, n_estimators=3;, score=0.641 total time= 0.0s
[CV 4/5; 33/400] START max_depth=1, min_samples_leaf=4, n_estimators=3..........
[CV 4/5; 33/400] END max_depth=1, min_samples_leaf=4, n_estimators=3;, score=0.769 total time= 0.0s
[CV 5/5; 33/400] START max_depth=1, min_samples_leaf=4, n_estimators=3..........
[CV 5/5; 33/400] END max_depth=1, min_samples_leaf=4, n_estimators=3;, score=0.763 total time= 0.0s
[CV 1/5; 34/400] START max_depth=1, min_samples_leaf=4, n_estimators=4..........
[CV 1/5; 34/400] END max_depth=1, min_samples_leaf=4, n_estimators=4;, score=0.750 total time= 0.0s
[CV 2/5; 34/400] START max_depth=1, min_samples_leaf=4, n_estimators=4..........
[CV 2/5; 34/400] END max_depth=1, min_samples_leaf=4, n_estimators=4;, score=0.624 total time= 0.0s
[CV 3/5; 34/400] START max_depth=1, min_samples_leaf=4, n_estimators=4..........
[CV 3/5; 34/400] END max_depth=1, min_samples_leaf=4, n_estimators=4;, score=0.608 total time= 0.0s
[CV 4/5; 34/400] START max_depth=1, min_samples_leaf=4, n_estimators=4..........
[CV 4/5; 34/400] END max_depth=1, min_samples_leaf=4, n_estimators=4;, score=0.744 total time= 0.0s
[CV 5/5; 34/400] START max_depth=1, min_samples_leaf=4, n_estimators=4..........
[CV 5/5; 34/400] END max_depth=1, min_samples_leaf=4, n_estimators=4;, score=0.757 total time= 0.0s
[CV 1/5; 35/400] START max_depth=1, min_samples_leaf=4, n_estimators=5..........
[CV 1/5; 35/400] END max_depth=1, min_samples_leaf=4, n_estimators=5;, score=0.731 total time= 0.0s
[CV 2/5; 35/400] START max_depth=1, min_samples_leaf=4, n_estimators=5..........
[CV 2/5; 35/400] END max_depth=1, min_samples_leaf=4, n_estimators=5;, score=0.788 total time= 0.0s
[CV 3/5; 35/400] START max_depth=1, min_samples_leaf=4, n_estimators=5..........
[CV 3/5; 35/400] END max_depth=1, min_samples_leaf=4, n_estimators=5;, score=0.778 total time= 0.0s
[CV 4/5; 35/400] START max_depth=1, min_samples_leaf=4, n_estimators=5..........
[CV 4/5; 35/400] END max_depth=1, min_samples_leaf=4, n_estimators=5;, score=0.788 total time= 0.0s
[CV 5/5; 35/400] START max_depth=1, min_samples_leaf=4, n_estimators=5..........
[CV 5/5; 35/400] END max_depth=1, min_samples_leaf=4, n_estimators=5;, score=0.722 total time= 0.0s
[CV 1/5; 36/400] START max_depth=1, min_samples_leaf=4, n_estimators=6..........
[CV 1/5; 36/400] END max_depth=1, min_samples_leaf=4, n_estimators=6;, score=0.791 total time= 0.0s
[CV 2/5; 36/400] START max_depth=1, min_samples_leaf=4, n_estimators=6..........
[CV 2/5; 36/400] END max_depth=1, min_samples_leaf=4, n_estimators=6;, score=0.788 total time= 0.0s
[CV 3/5; 36/400] START max_depth=1, min_samples_leaf=4, n_estimators=6..........
[CV 3/5; 36/400] END max_depth=1, min_samples_leaf=4, n_estimators=6;, score=0.603 total time= 0.0s
[CV 4/5; 36/400] START max_depth=1, min_samples_leaf=4, n_estimators=6..........
[CV 4/5; 36/400] END max_depth=1, min_samples_leaf=4, n_estimators=6;, score=0.745 total time= 0.0s
[CV 5/5; 36/400] START max_depth=1, min_samples_leaf=4, n_estimators=6..........
[CV 5/5; 36/400] END max_depth=1, min_samples_leaf=4, n_estimators=6;, score=0.769 total time= 0.0s
[CV 1/5; 37/400] START max_depth=1, min_samples_leaf=4, n_estimators=7..........
[CV 1/5; 37/400] END max_depth=1, min_samples_leaf=4, n_estimators=7;, score=0.780 total time= 0.0s
[CV 2/5; 37/400] START max_depth=1, min_samples_leaf=4, n_estimators=7..........
[CV 2/5; 37/400] END max_depth=1, min_samples_leaf=4, n_estimators=7;, score=0.774 total time= 0.0s
[CV 3/5; 37/400] START max_depth=1, min_samples_leaf=4, n_estimators=7..........
[CV 3/5; 37/400] END max_depth=1, min_samples_leaf=4, n_estimators=7;, score=0.780 total time= 0.0s
[CV 4/5; 37/400] START max_depth=1, min_samples_leaf=4, n_estimators=7..........
[CV 4/5; 37/400] END max_depth=1, min_samples_leaf=4, n_estimators=7;, score=0.647 total time= 0.0s
[CV 5/5; 37/400] START max_depth=1, min_samples_leaf=4, n_estimators=7..........
[CV 5/5; 37/400] END max_depth=1, min_samples_leaf=4, n_estimators=7;, score=0.777 total time= 0.0s
[CV 1/5; 38/400] START max_depth=1, min_samples_leaf=4, n_estimators=8..........
[CV 1/5; 38/400] END max_depth=1, min_samples_leaf=4, n_estimators=8;, score=0.794 total time= 0.0s
[CV 2/5; 38/400] START max_depth=1, min_samples_leaf=4, n_estimators=8..........
[CV 2/5; 38/400] END max_depth=1, min_samples_leaf=4, n_estimators=8;, score=0.720 total time= 0.0s
[CV 3/5; 38/400] START max_depth=1, min_samples_leaf=4, n_estimators=8..........
[CV 3/5; 38/400] END max_depth=1, min_samples_leaf=4, n_estimators=8;, score=0.780 total time= 0.0s
[CV 4/5; 38/400] START max_depth=1, min_samples_leaf=4, n_estimators=8..........
[CV 4/5; 38/400] END max_depth=1, min_samples_leaf=4, n_estimators=8;, score=0.769 total time= 0.0s
[CV 5/5; 38/400] START max_depth=1, min_samples_leaf=4, n_estimators=8..........
[CV 5/5; 38/400] END max_depth=1, min_samples_leaf=4, n_estimators=8;, score=0.716 total time= 0.0s
[CV 1/5; 39/400] START max_depth=1, min_samples_leaf=4, n_estimators=9..........
[CV 1/5; 39/400] END max_depth=1, min_samples_leaf=4, n_estimators=9;, score=0.785 total time= 0.0s
[CV 2/5; 39/400] START max_depth=1, min_samples_leaf=4, n_estimators=9..........
[CV 2/5; 39/400] END max_depth=1, min_samples_leaf=4, n_estimators=9;, score=0.788 total time= 0.0s
[CV 3/5; 39/400] START max_depth=1, min_samples_leaf=4, n_estimators=9..........
[CV 3/5; 39/400] END max_depth=1, min_samples_leaf=4, n_estimators=9;, score=0.780 total time= 0.0s
[CV 4/5; 39/400] START max_depth=1, min_samples_leaf=4, n_estimators=9..........
[CV 4/5; 39/400] END max_depth=1, min_samples_leaf=4, n_estimators=9;, score=0.742 total time= 0.0s
[CV 5/5; 39/400] START max_depth=1, min_samples_leaf=4, n_estimators=9..........
[CV 5/5; 39/400] END max_depth=1, min_samples_leaf=4, n_estimators=9;, score=0.764 total time= 0.0s
[CV 1/5; 40/400] START max_depth=1, min_samples_leaf=4, n_estimators=10.........
[CV 1/5; 40/400] END max_depth=1, min_samples_leaf=4, n_estimators=10;, score=0.597 total time= 0.0s
[CV 2/5; 40/400] START max_depth=1, min_samples_leaf=4, n_estimators=10.........
[CV 2/5; 40/400] END max_depth=1, min_samples_leaf=4, n_estimators=10;, score=0.760 total time= 0.0s
[CV 3/5; 40/400] START max_depth=1, min_samples_leaf=4, n_estimators=10.........
[CV 3/5; 40/400] END max_depth=1, min_samples_leaf=4, n_estimators=10;, score=0.771 total time= 0.0s
[CV 4/5; 40/400] START max_depth=1, min_samples_leaf=4, n_estimators=10.........
[CV 4/5; 40/400] END max_depth=1, min_samples_leaf=4, n_estimators=10;, score=0.788 total time= 0.0s
[CV 5/5; 40/400] START max_depth=1, min_samples_leaf=4, n_estimators=10.........
[CV 5/5; 40/400] END max_depth=1, min_samples_leaf=4, n_estimators=10;, score=0.777 total time= 0.0s
[CV 1/5; 41/400] START max_depth=2, min_samples_leaf=1, n_estimators=1..........
[CV 1/5; 41/400] END max_depth=2, min_samples_leaf=1, n_estimators=1;, score=0.641 total time= 0.0s
[CV 2/5; 41/400] START max_depth=2, min_samples_leaf=1, n_estimators=1..........
[CV 2/5; 41/400] END max_depth=2, min_samples_leaf=1, n_estimators=1;, score=0.794 total time= 0.0s
[CV 3/5; 41/400] START max_depth=2, min_samples_leaf=1, n_estimators=1..........
[CV 3/5; 41/400] END max_depth=2, min_samples_leaf=1, n_estimators=1;, score=0.780 total time= 0.0s
[CV 4/5; 41/400] START max_depth=2, min_samples_leaf=1, n_estimators=1..........
[CV 4/5; 41/400] END max_depth=2, min_samples_leaf=1, n_estimators=1;, score=0.790 total time= 0.0s
[CV 5/5; 41/400] START max_depth=2, min_samples_leaf=1, n_estimators=1..........
[CV 5/5; 41/400] END max_depth=2, min_samples_leaf=1, n_estimators=1;, score=0.769 total time= 0.0s
[CV 1/5; 42/400] START max_depth=2, min_samples_leaf=1, n_estimators=2..........
[CV 1/5; 42/400] END max_depth=2, min_samples_leaf=1, n_estimators=2;, score=0.763 total time= 0.0s
[CV 2/5; 42/400] START max_depth=2, min_samples_leaf=1, n_estimators=2..........
[CV 2/5; 42/400] END max_depth=2, min_samples_leaf=1, n_estimators=2;, score=0.741 total time= 0.0s
[CV 3/5; 42/400] START max_depth=2, min_samples_leaf=1, n_estimators=2..........
[CV 3/5; 42/400] END max_depth=2, min_samples_leaf=1, n_estimators=2;, score=0.773 total time= 0.0s
[CV 4/5; 42/400] START max_depth=2, min_samples_leaf=1, n_estimators=2..........
[CV 4/5; 42/400] END max_depth=2, min_samples_leaf=1, n_estimators=2;, score=0.786 total time= 0.0s
[CV 5/5; 42/400] START max_depth=2, min_samples_leaf=1, n_estimators=2..........
[CV 5/5; 42/400] END max_depth=2, min_samples_leaf=1, n_estimators=2;, score=0.709 total time= 0.0s
[CV 1/5; 43/400] START max_depth=2, min_samples_leaf=1, n_estimators=3..........
[CV 1/5; 43/400] END max_depth=2, min_samples_leaf=1, n_estimators=3;, score=0.796 total time= 0.0s
[CV 2/5; 43/400] START max_depth=2, min_samples_leaf=1, n_estimators=3..........
[CV 2/5; 43/400] END max_depth=2, min_samples_leaf=1, n_estimators=3;, score=0.769 total time= 0.0s
[CV 3/5; 43/400] START max_depth=2, min_samples_leaf=1, n_estimators=3..........
[CV 3/5; 43/400] END max_depth=2, min_samples_leaf=1, n_estimators=3;, score=0.760 total time= 0.0s
[CV 4/5; 43/400] START max_depth=2, min_samples_leaf=1, n_estimators=3..........
[CV 4/5; 43/400] END max_depth=2, min_samples_leaf=1, n_estimators=3;, score=0.790 total time= 0.0s
[CV 5/5; 43/400] START max_depth=2, min_samples_leaf=1, n_estimators=3..........
[CV 5/5; 43/400] END max_depth=2, min_samples_leaf=1, n_estimators=3;, score=0.771 total time= 0.0s
[CV 1/5; 44/400] START max_depth=2, min_samples_leaf=1, n_estimators=4..........
[CV 1/5; 44/400] END max_depth=2, min_samples_leaf=1, n_estimators=4;, score=0.758 total time= 0.0s
[CV 2/5; 44/400] START max_depth=2, min_samples_leaf=1, n_estimators=4..........
[CV 2/5; 44/400] END max_depth=2, min_samples_leaf=1, n_estimators=4;, score=0.788 total time= 0.0s
[CV 3/5; 44/400] START max_depth=2, min_samples_leaf=1, n_estimators=4..........
[CV 3/5; 44/400] END max_depth=2, min_samples_leaf=1, n_estimators=4;, score=0.780 total time= 0.0s
[CV 4/5; 44/400] START max_depth=2, min_samples_leaf=1, n_estimators=4..........
[CV 4/5; 44/400] END max_depth=2, min_samples_leaf=1, n_estimators=4;, score=0.744 total time= 0.0s
[CV 5/5; 44/400] START max_depth=2, min_samples_leaf=1, n_estimators=4..........
[CV 5/5; 44/400] END max_depth=2, min_samples_leaf=1, n_estimators=4;, score=0.694 total time= 0.0s
[CV 1/5; 45/400] START max_depth=2, min_samples_leaf=1, n_estimators=5..........
[CV 1/5; 45/400] END max_depth=2, min_samples_leaf=1, n_estimators=5;, score=0.794 total time= 0.0s
[CV 2/5; 45/400] START max_depth=2, min_samples_leaf=1, n_estimators=5..........
[CV 2/5; 45/400] END max_depth=2, min_samples_leaf=1, n_estimators=5;, score=0.766 total time= 0.0s
[CV 3/5; 45/400] START max_depth=2, min_samples_leaf=1, n_estimators=5..........
[CV 3/5; 45/400] END max_depth=2, min_samples_leaf=1, n_estimators=5;, score=0.780 total time= 0.0s
[CV 4/5; 45/400] START max_depth=2, min_samples_leaf=1, n_estimators=5..........
[CV 4/5; 45/400] END max_depth=2, min_samples_leaf=1, n_estimators=5;, score=0.773 total time= 0.0s
[CV 5/5; 45/400] START max_depth=2, min_samples_leaf=1, n_estimators=5..........
[CV 5/5; 45/400] END max_depth=2, min_samples_leaf=1, n_estimators=5;, score=0.768 total time= 0.0s
[CV 1/5; 46/400] START max_depth=2, min_samples_leaf=1, n_estimators=6..........
[CV 1/5; 46/400] END max_depth=2, min_samples_leaf=1, n_estimators=6;, score=0.745 total time= 0.0s
[CV 2/5; 46/400] START max_depth=2, min_samples_leaf=1, n_estimators=6..........
[CV 2/5; 46/400] END max_depth=2, min_samples_leaf=1, n_estimators=6;, score=0.790 total time= 0.0s
[CV 3/5; 46/400] START max_depth=2, min_samples_leaf=1, n_estimators=6..........
[CV 3/5; 46/400] END max_depth=2, min_samples_leaf=1, n_estimators=6;, score=0.764 total time= 0.0s
[CV 4/5; 46/400] START max_depth=2, min_samples_leaf=1, n_estimators=6..........
[CV 4/5; 46/400] END max_depth=2, min_samples_leaf=1, n_estimators=6;, score=0.790 total time= 0.0s
[CV 5/5; 46/400] START max_depth=2, min_samples_leaf=1, n_estimators=6..........
[CV 5/5; 46/400] END max_depth=2, min_samples_leaf=1, n_estimators=6;, score=0.777 total time= 0.0s
[CV 1/5; 47/400] START max_depth=2, min_samples_leaf=1, n_estimators=7..........
[CV 1/5; 47/400] END max_depth=2, min_samples_leaf=1, n_estimators=7;, score=0.763 total time= 0.0s
[CV 2/5; 47/400] START max_depth=2, min_samples_leaf=1, n_estimators=7..........
[CV 2/5; 47/400] END max_depth=2, min_samples_leaf=1, n_estimators=7;, score=0.783 total time= 0.0s
[CV 3/5; 47/400] START max_depth=2, min_samples_leaf=1, n_estimators=7..........
[CV 3/5; 47/400] END max_depth=2, min_samples_leaf=1, n_estimators=7;, score=0.780 total time= 0.0s
[CV 4/5; 47/400] START max_depth=2, min_samples_leaf=1, n_estimators=7..........
[CV 4/5; 47/400] END max_depth=2, min_samples_leaf=1, n_estimators=7;, score=0.789 total time= 0.0s
[CV 5/5; 47/400] START max_depth=2, min_samples_leaf=1, n_estimators=7..........
[CV 5/5; 47/400] END max_depth=2, min_samples_leaf=1, n_estimators=7;, score=0.772 total time= 0.0s
[CV 1/5; 48/400] START max_depth=2, min_samples_leaf=1, n_estimators=8..........
[CV 1/5; 48/400] END max_depth=2, min_samples_leaf=1, n_estimators=8;, score=0.769 total time= 0.0s
[CV 2/5; 48/400] START max_depth=2, min_samples_leaf=1, n_estimators=8..........
[CV 2/5; 48/400] END max_depth=2, min_samples_leaf=1, n_estimators=8;, score=0.773 total time= 0.0s
[CV 3/5; 48/400] START max_depth=2, min_samples_leaf=1, n_estimators=8..........
[CV 3/5; 48/400] END max_depth=2, min_samples_leaf=1, n_estimators=8;, score=0.780 total time= 0.0s
[CV 4/5; 48/400] START max_depth=2, min_samples_leaf=1, n_estimators=8..........
[CV 4/5; 48/400] END max_depth=2, min_samples_leaf=1, n_estimators=8;, score=0.795 total time= 0.0s
[CV 5/5; 48/400] START max_depth=2, min_samples_leaf=1, n_estimators=8..........
[CV 5/5; 48/400] END max_depth=2, min_samples_leaf=1, n_estimators=8;, score=0.758 total time= 0.0s
[CV 1/5; 49/400] START max_depth=2, min_samples_leaf=1, n_estimators=9..........
[CV 1/5; 49/400] END max_depth=2, min_samples_leaf=1, n_estimators=9;, score=0.794 total time= 0.0s
[CV 2/5; 49/400] START max_depth=2, min_samples_leaf=1, n_estimators=9..........
[CV 2/5; 49/400] END max_depth=2, min_samples_leaf=1, n_estimators=9;, score=0.782 total time= 0.0s
[CV 3/5; 49/400] START max_depth=2, min_samples_leaf=1, n_estimators=9..........
[CV 3/5; 49/400] END max_depth=2, min_samples_leaf=1, n_estimators=9;, score=0.777 total time= 0.0s
[CV 4/5; 49/400] START max_depth=2, min_samples_leaf=1, n_estimators=9..........
[CV 4/5; 49/400] END max_depth=2, min_samples_leaf=1, n_estimators=9;, score=0.790 total time= 0.0s
[CV 5/5; 49/400] START max_depth=2, min_samples_leaf=1, n_estimators=9..........
[CV 5/5; 49/400] END max_depth=2, min_samples_leaf=1, n_estimators=9;, score=0.773 total time= 0.0s
[CV 1/5; 50/400] START max_depth=2, min_samples_leaf=1, n_estimators=10.........
[CV 1/5; 50/400] END max_depth=2, min_samples_leaf=1, n_estimators=10;, score=0.789 total time= 0.0s
[CV 2/5; 50/400] START max_depth=2, min_samples_leaf=1, n_estimators=10.........
[CV 2/5; 50/400] END max_depth=2, min_samples_leaf=1, n_estimators=10;, score=0.788 total time= 0.0s
[CV 3/5; 50/400] START max_depth=2, min_samples_leaf=1, n_estimators=10.........
[CV 3/5; 50/400] END max_depth=2, min_samples_leaf=1, n_estimators=10;, score=0.780 total time= 0.0s
[CV 4/5; 50/400] START max_depth=2, min_samples_leaf=1, n_estimators=10.........
[CV 4/5; 50/400] END max_depth=2, min_samples_leaf=1, n_estimators=10;, score=0.789 total time= 0.0s
[CV 5/5; 50/400] START max_depth=2, min_samples_leaf=1, n_estimators=10.........
[CV 5/5; 50/400] END max_depth=2, min_samples_leaf=1, n_estimators=10;, score=0.778 total time= 0.0s
[CV 1/5; 51/400] START max_depth=2, min_samples_leaf=2, n_estimators=1..........
[CV 1/5; 51/400] END max_depth=2, min_samples_leaf=2, n_estimators=1;, score=0.774 total time= 0.0s
[CV 2/5; 51/400] START max_depth=2, min_samples_leaf=2, n_estimators=1..........
[CV 2/5; 51/400] END max_depth=2, min_samples_leaf=2, n_estimators=1;, score=0.597 total time= 0.0s
[CV 3/5; 51/400] START max_depth=2, min_samples_leaf=2, n_estimators=1..........
[CV 3/5; 51/400] END max_depth=2, min_samples_leaf=2, n_estimators=1;, score=0.604 total time= 0.0s
[CV 4/5; 51/400] START max_depth=2, min_samples_leaf=2, n_estimators=1..........
[CV 4/5; 51/400] END max_depth=2, min_samples_leaf=2, n_estimators=1;, score=0.788 total time= 0.0s
[CV 5/5; 51/400] START max_depth=2, min_samples_leaf=2, n_estimators=1..........
[CV 5/5; 51/400] END max_depth=2, min_samples_leaf=2, n_estimators=1;, score=0.613 total time= 0.0s
[CV 1/5; 52/400] START max_depth=2, min_samples_leaf=2, n_estimators=2..........
[CV 1/5; 52/400] END max_depth=2, min_samples_leaf=2, n_estimators=2;, score=0.625 total time= 0.0s
[CV 2/5; 52/400] START max_depth=2, min_samples_leaf=2, n_estimators=2..........
[CV 2/5; 52/400] END max_depth=2, min_samples_leaf=2, n_estimators=2;, score=0.752 total time= 0.0s
[CV 3/5; 52/400] START max_depth=2, min_samples_leaf=2, n_estimators=2..........
[CV 3/5; 52/400] END max_depth=2, min_samples_leaf=2, n_estimators=2;, score=0.776 total time= 0.0s
[CV 4/5; 52/400] START max_depth=2, min_samples_leaf=2, n_estimators=2..........
[CV 4/5; 52/400] END max_depth=2, min_samples_leaf=2, n_estimators=2;, score=0.791 total time= 0.0s
[CV 5/5; 52/400] START max_depth=2, min_samples_leaf=2, n_estimators=2..........
[CV 5/5; 52/400] END max_depth=2, min_samples_leaf=2, n_estimators=2;, score=0.773 total time= 0.0s
[CV 1/5; 53/400] START max_depth=2, min_samples_leaf=2, n_estimators=3..........
[CV 1/5; 53/400] END max_depth=2, min_samples_leaf=2, n_estimators=3;, score=0.785 total time= 0.0s
[CV 2/5; 53/400] START max_depth=2, min_samples_leaf=2, n_estimators=3..........
[CV 2/5; 53/400] END max_depth=2, min_samples_leaf=2, n_estimators=3;, score=0.740 total time= 0.0s
[CV 3/5; 53/400] START max_depth=2, min_samples_leaf=2, n_estimators=3..........
[CV 3/5; 53/400] END max_depth=2, min_samples_leaf=2, n_estimators=3;, score=0.766 total time= 0.0s
[CV 4/5; 53/400] START max_depth=2, min_samples_leaf=2, n_estimators=3..........
[CV 4/5; 53/400] END max_depth=2, min_samples_leaf=2, n_estimators=3;, score=0.788 total time= 0.0s
[CV 5/5; 53/400] START max_depth=2, min_samples_leaf=2, n_estimators=3..........
[CV 5/5; 53/400] END max_depth=2, min_samples_leaf=2, n_estimators=3;, score=0.779 total time= 0.0s
[CV 1/5; 54/400] START max_depth=2, min_samples_leaf=2, n_estimators=4..........
[CV 1/5; 54/400] END max_depth=2, min_samples_leaf=2, n_estimators=4;, score=0.788 total time= 0.0s
[CV 2/5; 54/400] START max_depth=2, min_samples_leaf=2, n_estimators=4..........
[CV 2/5; 54/400] END max_depth=2, min_samples_leaf=2, n_estimators=4;, score=0.792 total time= 0.0s
[CV 3/5; 54/400] START max_depth=2, min_samples_leaf=2, n_estimators=4..........
[CV 3/5; 54/400] END max_depth=2, min_samples_leaf=2, n_estimators=4;, score=0.760 total time= 0.0s
[CV 4/5; 54/400] START max_depth=2, min_samples_leaf=2, n_estimators=4..........
[CV 4/5; 54/400] END max_depth=2, min_samples_leaf=2, n_estimators=4;, score=0.789 total time= 0.0s
[CV 5/5; 54/400] START max_depth=2, min_samples_leaf=2, n_estimators=4..........
[CV 5/5; 54/400] END max_depth=2, min_samples_leaf=2, n_estimators=4;, score=0.770 total time= 0.0s
[CV 1/5; 55/400] START max_depth=2, min_samples_leaf=2, n_estimators=5..........
[CV 1/5; 55/400] END max_depth=2, min_samples_leaf=2, n_estimators=5;, score=0.787 total time= 0.0s
[CV 2/5; 55/400] START max_depth=2, min_samples_leaf=2, n_estimators=5..........
[CV 2/5; 55/400] END max_depth=2, min_samples_leaf=2, n_estimators=5;, score=0.788 total time= 0.0s
[CV 3/5; 55/400] START max_depth=2, min_samples_leaf=2, n_estimators=5..........
[CV 3/5; 55/400] END max_depth=2, min_samples_leaf=2, n_estimators=5;, score=0.780 total time= 0.0s
[CV 4/5; 55/400] START max_depth=2, min_samples_leaf=2, n_estimators=5..........
[CV 4/5; 55/400] END max_depth=2, min_samples_leaf=2, n_estimators=5;, score=0.790 total time= 0.0s
[CV 5/5; 55/400] START max_depth=2, min_samples_leaf=2, n_estimators=5..........
[CV 5/5; 55/400] END max_depth=2, min_samples_leaf=2, n_estimators=5;, score=0.760 total time= 0.0s
[CV 1/5; 56/400] START max_depth=2, min_samples_leaf=2, n_estimators=6..........
[CV 1/5; 56/400] END max_depth=2, min_samples_leaf=2, n_estimators=6;, score=0.714 total time= 0.0s
[CV 2/5; 56/400] START max_depth=2, min_samples_leaf=2, n_estimators=6..........
[CV 2/5; 56/400] END max_depth=2, min_samples_leaf=2, n_estimators=6;, score=0.788 total time= 0.0s
[CV 3/5; 56/400] START max_depth=2, min_samples_leaf=2, n_estimators=6..........
[CV 3/5; 56/400] END max_depth=2, min_samples_leaf=2, n_estimators=6;, score=0.780 total time= 0.0s
[CV 4/5; 56/400] START max_depth=2, min_samples_leaf=2, n_estimators=6..........
[CV 4/5; 56/400] END max_depth=2, min_samples_leaf=2, n_estimators=6;, score=0.790 total time= 0.0s
[CV 5/5; 56/400] START max_depth=2, min_samples_leaf=2, n_estimators=6..........
[CV 5/5; 56/400] END max_depth=2, min_samples_leaf=2, n_estimators=6;, score=0.769 total time= 0.0s
[CV 1/5; 57/400] START max_depth=2, min_samples_leaf=2, n_estimators=7..........
[CV 1/5; 57/400] END max_depth=2, min_samples_leaf=2, n_estimators=7;, score=0.796 total time= 0.0s
[CV 2/5; 57/400] START max_depth=2, min_samples_leaf=2, n_estimators=7..........
[CV 2/5; 57/400] END max_depth=2, min_samples_leaf=2, n_estimators=7;, score=0.791 total time= 0.0s
[CV 3/5; 57/400] START max_depth=2, min_samples_leaf=2, n_estimators=7..........
[CV 3/5; 57/400] END max_depth=2, min_samples_leaf=2, n_estimators=7;, score=0.780 total time= 0.0s
[CV 4/5; 57/400] START max_depth=2, min_samples_leaf=2, n_estimators=7..........
[CV 4/5; 57/400] END max_depth=2, min_samples_leaf=2, n_estimators=7;, score=0.791 total time= 0.0s
[CV 5/5; 57/400] START max_depth=2, min_samples_leaf=2, n_estimators=7..........
[CV 5/5; 57/400] END max_depth=2, min_samples_leaf=2, n_estimators=7;, score=0.772 total time= 0.0s
[CV 1/5; 58/400] START max_depth=2, min_samples_leaf=2, n_estimators=8..........
[CV 1/5; 58/400] END max_depth=2, min_samples_leaf=2, n_estimators=8;, score=0.789 total time= 0.0s
[CV 2/5; 58/400] START max_depth=2, min_samples_leaf=2, n_estimators=8..........
[CV 2/5; 58/400] END max_depth=2, min_samples_leaf=2, n_estimators=8;, score=0.788 total time= 0.0s
[CV 3/5; 58/400] START max_depth=2, min_samples_leaf=2, n_estimators=8..........
[CV 3/5; 58/400] END max_depth=2, min_samples_leaf=2, n_estimators=8;, score=0.774 total time= 0.0s
[CV 4/5; 58/400] START max_depth=2, min_samples_leaf=2, n_estimators=8..........
[CV 4/5; 58/400] END max_depth=2, min_samples_leaf=2, n_estimators=8;, score=0.792 total time= 0.0s
[CV 5/5; 58/400] START max_depth=2, min_samples_leaf=2, n_estimators=8..........
[CV 5/5; 58/400] END max_depth=2, min_samples_leaf=2, n_estimators=8;, score=0.772 total time= 0.0s
[CV 1/5; 59/400] START max_depth=2, min_samples_leaf=2, n_estimators=9..........
[CV 1/5; 59/400] END max_depth=2, min_samples_leaf=2, n_estimators=9;, score=0.794 total time= 0.0s
[CV 2/5; 59/400] START max_depth=2, min_samples_leaf=2, n_estimators=9..........
[CV 2/5; 59/400] END max_depth=2, min_samples_leaf=2, n_estimators=9;, score=0.788 total time= 0.0s
[CV 3/5; 59/400] START max_depth=2, min_samples_leaf=2, n_estimators=9..........
[CV 3/5; 59/400] END max_depth=2, min_samples_leaf=2, n_estimators=9;, score=0.776 total time= 0.0s
[CV 4/5; 59/400] START max_depth=2, min_samples_leaf=2, n_estimators=9..........
[CV 4/5; 59/400] END max_depth=2, min_samples_leaf=2, n_estimators=9;, score=0.768 total time= 0.0s
[CV 5/5; 59/400] START max_depth=2, min_samples_leaf=2, n_estimators=9..........
[CV 5/5; 59/400] END max_depth=2, min_samples_leaf=2, n_estimators=9;, score=0.777 total time= 0.0s
[CV 1/5; 60/400] START max_depth=2, min_samples_leaf=2, n_estimators=10.........
[CV 1/5; 60/400] END max_depth=2, min_samples_leaf=2, n_estimators=10;, score=0.796 total time= 0.0s
[CV 2/5; 60/400] START max_depth=2, min_samples_leaf=2, n_estimators=10.........
[CV 2/5; 60/400] END max_depth=2, min_samples_leaf=2, n_estimators=10;, score=0.788 total time= 0.1s
[CV 3/5; 60/400] START max_depth=2, min_samples_leaf=2, n_estimators=10.........
[CV 3/5; 60/400] END max_depth=2, min_samples_leaf=2, n_estimators=10;, score=0.782 total time= 0.0s
[CV 4/5; 60/400] START max_depth=2, min_samples_leaf=2, n_estimators=10.........
[CV 4/5; 60/400] END max_depth=2, min_samples_leaf=2, n_estimators=10;, score=0.793 total time= 0.0s
[CV 5/5; 60/400] START max_depth=2, min_samples_leaf=2, n_estimators=10.........
[CV 5/5; 60/400] END max_depth=2, min_samples_leaf=2, n_estimators=10;, score=0.774 total time= 0.0s
[CV 1/5; 61/400] START max_depth=2, min_samples_leaf=3, n_estimators=1..........
[CV 1/5; 61/400] END max_depth=2, min_samples_leaf=3, n_estimators=1;, score=0.787 total time= 0.0s
[CV 2/5; 61/400] START max_depth=2, min_samples_leaf=3, n_estimators=1..........
[CV 2/5; 61/400] END max_depth=2, min_samples_leaf=3, n_estimators=1;, score=0.630 total time= 0.0s
[CV 3/5; 61/400] START max_depth=2, min_samples_leaf=3, n_estimators=1..........
[CV 3/5; 61/400] END max_depth=2, min_samples_leaf=3, n_estimators=1;, score=0.613 total time= 0.0s
[CV 4/5; 61/400] START max_depth=2, min_samples_leaf=3, n_estimators=1..........
[CV 4/5; 61/400] END max_depth=2, min_samples_leaf=3, n_estimators=1;, score=0.790 total time= 0.0s
[CV 5/5; 61/400] START max_depth=2, min_samples_leaf=3, n_estimators=1..........
[CV 5/5; 61/400] END max_depth=2, min_samples_leaf=3, n_estimators=1;, score=0.589 total time= 0.0s
[CV 1/5; 62/400] START max_depth=2, min_samples_leaf=3, n_estimators=2..........
[CV 1/5; 62/400] END max_depth=2, min_samples_leaf=3, n_estimators=2;, score=0.597 total time= 0.0s
[CV 2/5; 62/400] START max_depth=2, min_samples_leaf=3, n_estimators=2..........
[CV 2/5; 62/400] END max_depth=2, min_samples_leaf=3, n_estimators=2;, score=0.672 total time= 0.0s
[CV 3/5; 62/400] START max_depth=2, min_samples_leaf=3, n_estimators=2..........
[CV 3/5; 62/400] END max_depth=2, min_samples_leaf=3, n_estimators=2;, score=0.771 total time= 0.0s
[CV 4/5; 62/400] START max_depth=2, min_samples_leaf=3, n_estimators=2..........
[CV 4/5; 62/400] END max_depth=2, min_samples_leaf=3, n_estimators=2;, score=0.790 total time= 0.0s
[CV 5/5; 62/400] START max_depth=2, min_samples_leaf=3, n_estimators=2..........
[CV 5/5; 62/400] END max_depth=2, min_samples_leaf=3, n_estimators=2;, score=0.771 total time= 0.0s
[CV 1/5; 63/400] START max_depth=2, min_samples_leaf=3, n_estimators=3..........
[CV 1/5; 63/400] END max_depth=2, min_samples_leaf=3, n_estimators=3;, score=0.756 total time= 0.0s
[CV 2/5; 63/400] START max_depth=2, min_samples_leaf=3, n_estimators=3..........
[CV 2/5; 63/400] END max_depth=2, min_samples_leaf=3, n_estimators=3;, score=0.786 total time= 0.0s
[CV 3/5; 63/400] START max_depth=2, min_samples_leaf=3, n_estimators=3..........
[CV 3/5; 63/400] END max_depth=2, min_samples_leaf=3, n_estimators=3;, score=0.780 total time= 0.0s
[CV 4/5; 63/400] START max_depth=2, min_samples_leaf=3, n_estimators=3..........
[CV 4/5; 63/400] END max_depth=2, min_samples_leaf=3, n_estimators=3;, score=0.761 total time= 0.0s
[CV 5/5; 63/400] START max_depth=2, min_samples_leaf=3, n_estimators=3..........
[CV 5/5; 63/400] END max_depth=2, min_samples_leaf=3, n_estimators=3;, score=0.770 total time= 0.0s
[CV 1/5; 64/400] START max_depth=2, min_samples_leaf=3, n_estimators=4..........
[CV 1/5; 64/400] END max_depth=2, min_samples_leaf=3, n_estimators=4;, score=0.796 total time= 0.0s
[CV 2/5; 64/400] START max_depth=2, min_samples_leaf=3, n_estimators=4..........
[CV 2/5; 64/400] END max_depth=2, min_samples_leaf=3, n_estimators=4;, score=0.793 total time= 0.0s
[CV 3/5; 64/400] START max_depth=2, min_samples_leaf=3, n_estimators=4..........
[CV 3/5; 64/400] END max_depth=2, min_samples_leaf=3, n_estimators=4;, score=0.781 total time= 0.0s
[CV 4/5; 64/400] START max_depth=2, min_samples_leaf=3, n_estimators=4..........
[CV 4/5; 64/400] END max_depth=2, min_samples_leaf=3, n_estimators=4;, score=0.790 total time= 0.0s
[CV 5/5; 64/400] START max_depth=2, min_samples_leaf=3, n_estimators=4..........
[CV 5/5; 64/400] END max_depth=2, min_samples_leaf=3, n_estimators=4;, score=0.776 total time= 0.0s
[CV 1/5; 65/400] START max_depth=2, min_samples_leaf=3, n_estimators=5..........
[CV 1/5; 65/400] END max_depth=2, min_samples_leaf=3, n_estimators=5;, score=0.753 total time= 0.0s
[CV 2/5; 65/400] START max_depth=2, min_samples_leaf=3, n_estimators=5..........
[CV 2/5; 65/400] END max_depth=2, min_samples_leaf=3, n_estimators=5;, score=0.794 total time= 0.0s
[CV 3/5; 65/400] START max_depth=2, min_samples_leaf=3, n_estimators=5..........
[CV 3/5; 65/400] END max_depth=2, min_samples_leaf=3, n_estimators=5;, score=0.770 total time= 0.0s
[CV 4/5; 65/400] START max_depth=2, min_samples_leaf=3, n_estimators=5..........
[CV 4/5; 65/400] END max_depth=2, min_samples_leaf=3, n_estimators=5;, score=0.790 total time= 0.0s
[CV 5/5; 65/400] START max_depth=2, min_samples_leaf=3, n_estimators=5..........
[CV 5/5; 65/400] END max_depth=2, min_samples_leaf=3, n_estimators=5;, score=0.766 total time= 0.0s
[CV 1/5; 66/400] START max_depth=2, min_samples_leaf=3, n_estimators=6..........
[CV 1/5; 66/400] END max_depth=2, min_samples_leaf=3, n_estimators=6;, score=0.799 total time= 0.0s
[CV 2/5; 66/400] START max_depth=2, min_samples_leaf=3, n_estimators=6..........
[CV 2/5; 66/400] END max_depth=2, min_samples_leaf=3, n_estimators=6;, score=0.788 total time= 0.0s
[CV 3/5; 66/400] START max_depth=2, min_samples_leaf=3, n_estimators=6..........
[CV 3/5; 66/400] END max_depth=2, min_samples_leaf=3, n_estimators=6;, score=0.778 total time= 0.0s
[CV 4/5; 66/400] START max_depth=2, min_samples_leaf=3, n_estimators=6..........
[CV 4/5; 66/400] END max_depth=2, min_samples_leaf=3, n_estimators=6;, score=0.785 total time= 0.0s
[CV 5/5; 66/400] START max_depth=2, min_samples_leaf=3, n_estimators=6..........
[CV 5/5; 66/400] END max_depth=2, min_samples_leaf=3, n_estimators=6;, score=0.771 total time= 0.0s
[CV 1/5; 67/400] START max_depth=2, min_samples_leaf=3, n_estimators=7..........
[CV 1/5; 67/400] END max_depth=2, min_samples_leaf=3, n_estimators=7;, score=0.799 total time= 0.0s
[CV 2/5; 67/400] START max_depth=2, min_samples_leaf=3, n_estimators=7..........
[CV 2/5; 67/400] END max_depth=2, min_samples_leaf=3, n_estimators=7;, score=0.785 total time= 0.0s
[CV 3/5; 67/400] START max_depth=2, min_samples_leaf=3, n_estimators=7..........
[CV 3/5; 67/400] END max_depth=2, min_samples_leaf=3, n_estimators=7;, score=0.752 total time= 0.0s
[CV 4/5; 67/400] START max_depth=2, min_samples_leaf=3, n_estimators=7..........
[CV 4/5; 67/400] END max_depth=2, min_samples_leaf=3, n_estimators=7;, score=0.793 total time= 0.0s
[CV 5/5; 67/400] START max_depth=2, min_samples_leaf=3, n_estimators=7..........
[CV 5/5; 67/400] END max_depth=2, min_samples_leaf=3, n_estimators=7;, score=0.734 total time= 0.0s
[CV 1/5; 68/400] START max_depth=2, min_samples_leaf=3, n_estimators=8..........
[CV 1/5; 68/400] END max_depth=2, min_samples_leaf=3, n_estimators=8;, score=0.743 total time= 0.0s
[CV 2/5; 68/400] START max_depth=2, min_samples_leaf=3, n_estimators=8..........
[CV 2/5; 68/400] END max_depth=2, min_samples_leaf=3, n_estimators=8;, score=0.768 total time= 0.0s
[CV 3/5; 68/400] START max_depth=2, min_samples_leaf=3, n_estimators=8..........
[CV 3/5; 68/400] END max_depth=2, min_samples_leaf=3, n_estimators=8;, score=0.752 total time= 0.0s
[CV 4/5; 68/400] START max_depth=2, min_samples_leaf=3, n_estimators=8..........
[CV 4/5; 68/400] END max_depth=2, min_samples_leaf=3, n_estimators=8;, score=0.788 total time= 0.0s
[CV 5/5; 68/400] START max_depth=2, min_samples_leaf=3, n_estimators=8..........
[CV 5/5; 68/400] END max_depth=2, min_samples_leaf=3, n_estimators=8;, score=0.770 total time= 0.0s
[CV 1/5; 69/400] START max_depth=2, min_samples_leaf=3, n_estimators=9..........
[CV 1/5; 69/400] END max_depth=2, min_samples_leaf=3, n_estimators=9;, score=0.791 total time= 0.1s
[CV 2/5; 69/400] START max_depth=2, min_samples_leaf=3, n_estimators=9..........
[CV 2/5; 69/400] END max_depth=2, min_samples_leaf=3, n_estimators=9;, score=0.794 total time= 0.0s
[CV 3/5; 69/400] START max_depth=2, min_samples_leaf=3, n_estimators=9..........
[CV 3/5; 69/400] END max_depth=2, min_samples_leaf=3, n_estimators=9;, score=0.782 total time= 0.0s
[CV 4/5; 69/400] START max_depth=2, min_samples_leaf=3, n_estimators=9..........
[CV 4/5; 69/400] END max_depth=2, min_samples_leaf=3, n_estimators=9;, score=0.791 total time= 0.0s
[CV 5/5; 69/400] START max_depth=2, min_samples_leaf=3, n_estimators=9..........
[CV 5/5; 69/400] END max_depth=2, min_samples_leaf=3, n_estimators=9;, score=0.777 total time= 0.0s
[CV 1/5; 70/400] START max_depth=2, min_samples_leaf=3, n_estimators=10.........
[CV 1/5; 70/400] END max_depth=2, min_samples_leaf=3, n_estimators=10;, score=0.791 total time= 0.0s
[CV 2/5; 70/400] START max_depth=2, min_samples_leaf=3, n_estimators=10.........
[CV 2/5; 70/400] END max_depth=2, min_samples_leaf=3, n_estimators=10;, score=0.789 total time= 0.0s
[CV 3/5; 70/400] START max_depth=2, min_samples_leaf=3, n_estimators=10.........
[CV 3/5; 70/400] END max_depth=2, min_samples_leaf=3, n_estimators=10;, score=0.767 total time= 0.0s
[CV 4/5; 70/400] START max_depth=2, min_samples_leaf=3, n_estimators=10.........
[CV 4/5; 70/400] END max_depth=2, min_samples_leaf=3, n_estimators=10;, score=0.788 total time= 0.0s
[CV 5/5; 70/400] START max_depth=2, min_samples_leaf=3, n_estimators=10.........
[CV 5/5; 70/400] END max_depth=2, min_samples_leaf=3, n_estimators=10;, score=0.776 total time= 0.0s
[CV 1/5; 71/400] START max_depth=2, min_samples_leaf=4, n_estimators=1..........
[CV 1/5; 71/400] END max_depth=2, min_samples_leaf=4, n_estimators=1;, score=0.788 total time= 0.0s
[CV 2/5; 71/400] START max_depth=2, min_samples_leaf=4, n_estimators=1..........
[CV 2/5; 71/400] END max_depth=2, min_samples_leaf=4, n_estimators=1;, score=0.734 total time= 0.0s
[CV 3/5; 71/400] START max_depth=2, min_samples_leaf=4, n_estimators=1..........
[CV 3/5; 71/400] END max_depth=2, min_samples_leaf=4, n_estimators=1;, score=0.755 total time= 0.0s
[CV 4/5; 71/400] START max_depth=2, min_samples_leaf=4, n_estimators=1..........
[CV 4/5; 71/400] END max_depth=2, min_samples_leaf=4, n_estimators=1;, score=0.756 total time= 0.0s
[CV 5/5; 71/400] START max_depth=2, min_samples_leaf=4, n_estimators=1..........
[CV 5/5; 71/400] END max_depth=2, min_samples_leaf=4, n_estimators=1;, score=0.769 total time= 0.0s
[CV 1/5; 72/400] START max_depth=2, min_samples_leaf=4, n_estimators=2..........
[CV 1/5; 72/400] END max_depth=2, min_samples_leaf=4, n_estimators=2;, score=0.791 total time= 0.0s
[CV 2/5; 72/400] START max_depth=2, min_samples_leaf=4, n_estimators=2..........
[CV 2/5; 72/400] END max_depth=2, min_samples_leaf=4, n_estimators=2;, score=0.789 total time= 0.0s
[CV 3/5; 72/400] START max_depth=2, min_samples_leaf=4, n_estimators=2..........
[CV 3/5; 72/400] END max_depth=2, min_samples_leaf=4, n_estimators=2;, score=0.780 total time= 0.0s
[CV 4/5; 72/400] START max_depth=2, min_samples_leaf=4, n_estimators=2..........
[CV 4/5; 72/400] END max_depth=2, min_samples_leaf=4, n_estimators=2;, score=0.790 total time= 0.0s
[CV 5/5; 72/400] START max_depth=2, min_samples_leaf=4, n_estimators=2..........
[CV 5/5; 72/400] END max_depth=2, min_samples_leaf=4, n_estimators=2;, score=0.602 total time= 0.0s
[CV 1/5; 73/400] START max_depth=2, min_samples_leaf=4, n_estimators=3..........
[CV 1/5; 73/400] END max_depth=2, min_samples_leaf=4, n_estimators=3;, score=0.771 total time= 0.0s
[CV 2/5; 73/400] START max_depth=2, min_samples_leaf=4, n_estimators=3..........
[CV 2/5; 73/400] END max_depth=2, min_samples_leaf=4, n_estimators=3;, score=0.790 total time= 0.0s
[CV 3/5; 73/400] START max_depth=2, min_samples_leaf=4, n_estimators=3..........
[CV 3/5; 73/400] END max_depth=2, min_samples_leaf=4, n_estimators=3;, score=0.783 total time= 0.0s
[CV 4/5; 73/400] START max_depth=2, min_samples_leaf=4, n_estimators=3..........
[CV 4/5; 73/400] END max_depth=2, min_samples_leaf=4, n_estimators=3;, score=0.794 total time= 0.0s
[CV 5/5; 73/400] START max_depth=2, min_samples_leaf=4, n_estimators=3..........
[CV 5/5; 73/400] END max_depth=2, min_samples_leaf=4, n_estimators=3;, score=0.771 total time= 0.0s
[CV 1/5; 74/400] START max_depth=2, min_samples_leaf=4, n_estimators=4..........
[CV 1/5; 74/400] END max_depth=2, min_samples_leaf=4, n_estimators=4;, score=0.791 total time= 0.0s
[CV 2/5; 74/400] START max_depth=2, min_samples_leaf=4, n_estimators=4..........
[CV 2/5; 74/400] END max_depth=2, min_samples_leaf=4, n_estimators=4;, score=0.788 total time= 0.0s
[CV 3/5; 74/400] START max_depth=2, min_samples_leaf=4, n_estimators=4..........
[CV 3/5; 74/400] END max_depth=2, min_samples_leaf=4, n_estimators=4;, score=0.780 total time= 0.0s
[CV 4/5; 74/400] START max_depth=2, min_samples_leaf=4, n_estimators=4..........
[CV 4/5; 74/400] END max_depth=2, min_samples_leaf=4, n_estimators=4;, score=0.785 total time= 0.0s
[CV 5/5; 74/400] START max_depth=2, min_samples_leaf=4, n_estimators=4..........
[CV 5/5; 74/400] END max_depth=2, min_samples_leaf=4, n_estimators=4;, score=0.770 total time= 0.0s
[CV 1/5; 75/400] START max_depth=2, min_samples_leaf=4, n_estimators=5..........
[CV 1/5; 75/400] END max_depth=2, min_samples_leaf=4, n_estimators=5;, score=0.797 total time= 0.0s
[CV 2/5; 75/400] START max_depth=2, min_samples_leaf=4, n_estimators=5..........
[CV 2/5; 75/400] END max_depth=2, min_samples_leaf=4, n_estimators=5;, score=0.794 total time= 0.0s
[CV 3/5; 75/400] START max_depth=2, min_samples_leaf=4, n_estimators=5..........
[CV 3/5; 75/400] END max_depth=2, min_samples_leaf=4, n_estimators=5;, score=0.780 total time= 0.0s
[CV 4/5; 75/400] START max_depth=2, min_samples_leaf=4, n_estimators=5..........
[CV 4/5; 75/400] END max_depth=2, min_samples_leaf=4, n_estimators=5;, score=0.790 total time= 0.0s
[CV 5/5; 75/400] START max_depth=2, min_samples_leaf=4, n_estimators=5..........
[CV 5/5; 75/400] END max_depth=2, min_samples_leaf=4, n_estimators=5;, score=0.764 total time= 0.0s
[CV 1/5; 76/400] START max_depth=2, min_samples_leaf=4, n_estimators=6..........
[CV 1/5; 76/400] END max_depth=2, min_samples_leaf=4, n_estimators=6;, score=0.784 total time= 0.0s
[CV 2/5; 76/400] START max_depth=2, min_samples_leaf=4, n_estimators=6..........
[CV 2/5; 76/400] END max_depth=2, min_samples_leaf=4, n_estimators=6;, score=0.796 total time= 0.0s
[CV 3/5; 76/400] START max_depth=2, min_samples_leaf=4, n_estimators=6..........
[CV 3/5; 76/400] END max_depth=2, min_samples_leaf=4, n_estimators=6;, score=0.775 total time= 0.0s
[CV 4/5; 76/400] START max_depth=2, min_samples_leaf=4, n_estimators=6..........
[CV 4/5; 76/400] END max_depth=2, min_samples_leaf=4, n_estimators=6;, score=0.790 total time= 0.0s
[CV 5/5; 76/400] START max_depth=2, min_samples_leaf=4, n_estimators=6..........
[CV 5/5; 76/400] END max_depth=2, min_samples_leaf=4, n_estimators=6;, score=0.771 total time= 0.0s
[CV 1/5; 77/400] START max_depth=2, min_samples_leaf=4, n_estimators=7..........
[CV 1/5; 77/400] END max_depth=2, min_samples_leaf=4, n_estimators=7;, score=0.794 total time= 0.0s
[CV 2/5; 77/400] START max_depth=2, min_samples_leaf=4, n_estimators=7..........
[CV 2/5; 77/400] END max_depth=2, min_samples_leaf=4, n_estimators=7;, score=0.787 total time= 0.0s
[CV 3/5; 77/400] START max_depth=2, min_samples_leaf=4, n_estimators=7..........
[CV 3/5; 77/400] END max_depth=2, min_samples_leaf=4, n_estimators=7;, score=0.775 total time= 0.0s
[CV 4/5; 77/400] START max_depth=2, min_samples_leaf=4, n_estimators=7..........
[CV 4/5; 77/400] END max_depth=2, min_samples_leaf=4, n_estimators=7;, score=0.790 total time= 0.0s
[CV 5/5; 77/400] START max_depth=2, min_samples_leaf=4, n_estimators=7..........
[CV 5/5; 77/400] END max_depth=2, min_samples_leaf=4, n_estimators=7;, score=0.767 total time= 0.0s
[CV 1/5; 78/400] START max_depth=2, min_samples_leaf=4, n_estimators=8..........
[CV 1/5; 78/400] END max_depth=2, min_samples_leaf=4, n_estimators=8;, score=0.793 total time= 0.0s
[CV 2/5; 78/400] START max_depth=2, min_samples_leaf=4, n_estimators=8..........
[CV 2/5; 78/400] END max_depth=2, min_samples_leaf=4, n_estimators=8;, score=0.757 total time= 0.0s
[CV 3/5; 78/400] START max_depth=2, min_samples_leaf=4, n_estimators=8..........
[CV 3/5; 78/400] END max_depth=2, min_samples_leaf=4, n_estimators=8;, score=0.774 total time= 0.0s
[CV 4/5; 78/400] START max_depth=2, min_samples_leaf=4, n_estimators=8..........
[CV 4/5; 78/400] END max_depth=2, min_samples_leaf=4, n_estimators=8;, score=0.791 total time= 0.0s
[CV 5/5; 78/400] START max_depth=2, min_samples_leaf=4, n_estimators=8..........
[CV 5/5; 78/400] END max_depth=2, min_samples_leaf=4, n_estimators=8;, score=0.771 total time= 0.0s
[CV 1/5; 79/400] START max_depth=2, min_samples_leaf=4, n_estimators=9..........
[CV 1/5; 79/400] END max_depth=2, min_samples_leaf=4, n_estimators=9;, score=0.783 total time= 0.0s
[CV 2/5; 79/400] START max_depth=2, min_samples_leaf=4, n_estimators=9..........
[CV 2/5; 79/400] END max_depth=2, min_samples_leaf=4, n_estimators=9;, score=0.789 total time= 0.0s
[CV 3/5; 79/400] START max_depth=2, min_samples_leaf=4, n_estimators=9..........
[CV 3/5; 79/400] END max_depth=2, min_samples_leaf=4, n_estimators=9;, score=0.780 total time= 0.0s
[CV 4/5; 79/400] START max_depth=2, min_samples_leaf=4, n_estimators=9..........
[CV 4/5; 79/400] END max_depth=2, min_samples_leaf=4, n_estimators=9;, score=0.788 total time= 0.0s
[CV 5/5; 79/400] START max_depth=2, min_samples_leaf=4, n_estimators=9..........
[CV 5/5; 79/400] END max_depth=2, min_samples_leaf=4, n_estimators=9;, score=0.771 total time= 0.0s
[CV 1/5; 80/400] START max_depth=2, min_samples_leaf=4, n_estimators=10.........
[CV 1/5; 80/400] END max_depth=2, min_samples_leaf=4, n_estimators=10;, score=0.791 total time= 0.0s
[CV 2/5; 80/400] START max_depth=2, min_samples_leaf=4, n_estimators=10.........
[CV 2/5; 80/400] END max_depth=2, min_samples_leaf=4, n_estimators=10;, score=0.788 total time= 0.0s
[CV 3/5; 80/400] START max_depth=2, min_samples_leaf=4, n_estimators=10.........
[CV 3/5; 80/400] END max_depth=2, min_samples_leaf=4, n_estimators=10;, score=0.780 total time= 0.0s
[CV 4/5; 80/400] START max_depth=2, min_samples_leaf=4, n_estimators=10.........
[CV 4/5; 80/400] END max_depth=2, min_samples_leaf=4, n_estimators=10;, score=0.781 total time= 0.0s
[CV 5/5; 80/400] START max_depth=2, min_samples_leaf=4, n_estimators=10.........
[CV 5/5; 80/400] END max_depth=2, min_samples_leaf=4, n_estimators=10;, score=0.777 total time= 0.0s
[CV 1/5; 81/400] START max_depth=3, min_samples_leaf=1, n_estimators=1..........
[CV 1/5; 81/400] END max_depth=3, min_samples_leaf=1, n_estimators=1;, score=0.783 total time= 0.0s
[CV 2/5; 81/400] START max_depth=3, min_samples_leaf=1, n_estimators=1..........
[CV 2/5; 81/400] END max_depth=3, min_samples_leaf=1, n_estimators=1;, score=0.624 total time= 0.0s
[CV 3/5; 81/400] START max_depth=3, min_samples_leaf=1, n_estimators=1..........
[CV 3/5; 81/400] END max_depth=3, min_samples_leaf=1, n_estimators=1;, score=0.780 total time= 0.0s
[CV 4/5; 81/400] START max_depth=3, min_samples_leaf=1, n_estimators=1..........
[CV 4/5; 81/400] END max_depth=3, min_samples_leaf=1, n_estimators=1;, score=0.742 total time= 0.0s
[CV 5/5; 81/400] START max_depth=3, min_samples_leaf=1, n_estimators=1..........
[CV 5/5; 81/400] END max_depth=3, min_samples_leaf=1, n_estimators=1;, score=0.652 total time= 0.0s
[CV 1/5; 82/400] START max_depth=3, min_samples_leaf=1, n_estimators=2..........
[CV 1/5; 82/400] END max_depth=3, min_samples_leaf=1, n_estimators=2;, score=0.798 total time= 0.0s
[CV 2/5; 82/400] START max_depth=3, min_samples_leaf=1, n_estimators=2..........
[CV 2/5; 82/400] END max_depth=3, min_samples_leaf=1, n_estimators=2;, score=0.794 total time= 0.0s
[CV 3/5; 82/400] START max_depth=3, min_samples_leaf=1, n_estimators=2..........
[CV 3/5; 82/400] END max_depth=3, min_samples_leaf=1, n_estimators=2;, score=0.780 total time= 0.0s
[CV 4/5; 82/400] START max_depth=3, min_samples_leaf=1, n_estimators=2..........
[CV 4/5; 82/400] END max_depth=3, min_samples_leaf=1, n_estimators=2;, score=0.786 total time= 0.0s
[CV 5/5; 82/400] START max_depth=3, min_samples_leaf=1, n_estimators=2..........
[CV 5/5; 82/400] END max_depth=3, min_samples_leaf=1, n_estimators=2;, score=0.775 total time= 0.0s
[CV 1/5; 83/400] START max_depth=3, min_samples_leaf=1, n_estimators=3..........
[CV 1/5; 83/400] END max_depth=3, min_samples_leaf=1, n_estimators=3;, score=0.795 total time= 0.0s
[CV 2/5; 83/400] START max_depth=3, min_samples_leaf=1, n_estimators=3..........
[CV 2/5; 83/400] END max_depth=3, min_samples_leaf=1, n_estimators=3;, score=0.788 total time= 0.0s
[CV 3/5; 83/400] START max_depth=3, min_samples_leaf=1, n_estimators=3..........
[CV 3/5; 83/400] END max_depth=3, min_samples_leaf=1, n_estimators=3;, score=0.771 total time= 0.0s
[CV 4/5; 83/400] START max_depth=3, min_samples_leaf=1, n_estimators=3..........
[CV 4/5; 83/400] END max_depth=3, min_samples_leaf=1, n_estimators=3;, score=0.778 total time= 0.0s
[CV 5/5; 83/400] START max_depth=3, min_samples_leaf=1, n_estimators=3..........
[CV 5/5; 83/400] END max_depth=3, min_samples_leaf=1, n_estimators=3;, score=0.771 total time= 0.0s
[CV 1/5; 84/400] START max_depth=3, min_samples_leaf=1, n_estimators=4..........
[CV 1/5; 84/400] END max_depth=3, min_samples_leaf=1, n_estimators=4;, score=0.796 total time= 0.0s
[CV 2/5; 84/400] START max_depth=3, min_samples_leaf=1, n_estimators=4..........
[CV 2/5; 84/400] END max_depth=3, min_samples_leaf=1, n_estimators=4;, score=0.784 total time= 0.0s
[CV 3/5; 84/400] START max_depth=3, min_samples_leaf=1, n_estimators=4..........
[CV 3/5; 84/400] END max_depth=3, min_samples_leaf=1, n_estimators=4;, score=0.780 total time= 0.0s
[CV 4/5; 84/400] START max_depth=3, min_samples_leaf=1, n_estimators=4..........
[CV 4/5; 84/400] END max_depth=3, min_samples_leaf=1, n_estimators=4;, score=0.782 total time= 0.0s
[CV 5/5; 84/400] START max_depth=3, min_samples_leaf=1, n_estimators=4..........
[CV 5/5; 84/400] END max_depth=3, min_samples_leaf=1, n_estimators=4;, score=0.785 total time= 0.0s
[CV 1/5; 85/400] START max_depth=3, min_samples_leaf=1, n_estimators=5..........
[CV 1/5; 85/400] END max_depth=3, min_samples_leaf=1, n_estimators=5;, score=0.779 total time= 0.0s
[CV 2/5; 85/400] START max_depth=3, min_samples_leaf=1, n_estimators=5..........
[CV 2/5; 85/400] END max_depth=3, min_samples_leaf=1, n_estimators=5;, score=0.797 total time= 0.0s
[CV 3/5; 85/400] START max_depth=3, min_samples_leaf=1, n_estimators=5..........
[CV 3/5; 85/400] END max_depth=3, min_samples_leaf=1, n_estimators=5;, score=0.771 total time= 0.0s
[CV 4/5; 85/400] START max_depth=3, min_samples_leaf=1, n_estimators=5..........
[CV 4/5; 85/400] END max_depth=3, min_samples_leaf=1, n_estimators=5;, score=0.794 total time= 0.0s
[CV 5/5; 85/400] START max_depth=3, min_samples_leaf=1, n_estimators=5..........
[CV 5/5; 85/400] END max_depth=3, min_samples_leaf=1, n_estimators=5;, score=0.775 total time= 0.0s
[CV 1/5; 86/400] START max_depth=3, min_samples_leaf=1, n_estimators=6..........
[CV 1/5; 86/400] END max_depth=3, min_samples_leaf=1, n_estimators=6;, score=0.791 total time= 0.0s
[CV 2/5; 86/400] START max_depth=3, min_samples_leaf=1, n_estimators=6..........
[CV 2/5; 86/400] END max_depth=3, min_samples_leaf=1, n_estimators=6;, score=0.789 total time= 0.0s
[CV 3/5; 86/400] START max_depth=3, min_samples_leaf=1, n_estimators=6..........
[CV 3/5; 86/400] END max_depth=3, min_samples_leaf=1, n_estimators=6;, score=0.784 total time= 0.0s
[CV 4/5; 86/400] START max_depth=3, min_samples_leaf=1, n_estimators=6..........
[CV 4/5; 86/400] END max_depth=3, min_samples_leaf=1, n_estimators=6;, score=0.790 total time= 0.0s
[CV 5/5; 86/400] START max_depth=3, min_samples_leaf=1, n_estimators=6..........
[CV 5/5; 86/400] END max_depth=3, min_samples_leaf=1, n_estimators=6;, score=0.767 total time= 0.0s
[CV 1/5; 87/400] START max_depth=3, min_samples_leaf=1, n_estimators=7..........
[CV 1/5; 87/400] END max_depth=3, min_samples_leaf=1, n_estimators=7;, score=0.766 total time= 0.0s
[CV 2/5; 87/400] START max_depth=3, min_samples_leaf=1, n_estimators=7..........
[CV 2/5; 87/400] END max_depth=3, min_samples_leaf=1, n_estimators=7;, score=0.791 total time= 0.0s
[CV 3/5; 87/400] START max_depth=3, min_samples_leaf=1, n_estimators=7..........
[CV 3/5; 87/400] END max_depth=3, min_samples_leaf=1, n_estimators=7;, score=0.780 total time= 0.0s
[CV 4/5; 87/400] START max_depth=3, min_samples_leaf=1, n_estimators=7..........
[CV 4/5; 87/400] END max_depth=3, min_samples_leaf=1, n_estimators=7;, score=0.792 total time= 0.0s
[CV 5/5; 87/400] START max_depth=3, min_samples_leaf=1, n_estimators=7..........
[CV 5/5; 87/400] END max_depth=3, min_samples_leaf=1, n_estimators=7;, score=0.775 total time= 0.0s
[CV 1/5; 88/400] START max_depth=3, min_samples_leaf=1, n_estimators=8..........
[CV 1/5; 88/400] END max_depth=3, min_samples_leaf=1, n_estimators=8;, score=0.788 total time= 0.0s
[CV 2/5; 88/400] START max_depth=3, min_samples_leaf=1, n_estimators=8..........
[CV 2/5; 88/400] END max_depth=3, min_samples_leaf=1, n_estimators=8;, score=0.785 total time= 0.0s
[CV 3/5; 88/400] START max_depth=3, min_samples_leaf=1, n_estimators=8..........
[CV 3/5; 88/400] END max_depth=3, min_samples_leaf=1, n_estimators=8;, score=0.781 total time= 0.0s
[CV 4/5; 88/400] START max_depth=3, min_samples_leaf=1, n_estimators=8..........
[CV 4/5; 88/400] END max_depth=3, min_samples_leaf=1, n_estimators=8;, score=0.791 total time= 0.0s
[CV 5/5; 88/400] START max_depth=3, min_samples_leaf=1, n_estimators=8..........
[CV 5/5; 88/400] END max_depth=3, min_samples_leaf=1, n_estimators=8;, score=0.763 total time= 0.0s
[CV 1/5; 89/400] START max_depth=3, min_samples_leaf=1, n_estimators=9..........
[CV 1/5; 89/400] END max_depth=3, min_samples_leaf=1, n_estimators=9;, score=0.797 total time= 0.0s
[CV 2/5; 89/400] START max_depth=3, min_samples_leaf=1, n_estimators=9..........
[CV 2/5; 89/400] END max_depth=3, min_samples_leaf=1, n_estimators=9;, score=0.788 total time= 0.0s
[CV 3/5; 89/400] START max_depth=3, min_samples_leaf=1, n_estimators=9..........
[CV 3/5; 89/400] END max_depth=3, min_samples_leaf=1, n_estimators=9;, score=0.784 total time= 0.0s
[CV 4/5; 89/400] START max_depth=3, min_samples_leaf=1, n_estimators=9..........
[CV 4/5; 89/400] END max_depth=3, min_samples_leaf=1, n_estimators=9;, score=0.791 total time= 0.0s
[CV 5/5; 89/400] START max_depth=3, min_samples_leaf=1, n_estimators=9..........
[CV 5/5; 89/400] END max_depth=3, min_samples_leaf=1, n_estimators=9;, score=0.777 total time= 0.0s
[CV 1/5; 90/400] START max_depth=3, min_samples_leaf=1, n_estimators=10.........
[CV 1/5; 90/400] END max_depth=3, min_samples_leaf=1, n_estimators=10;, score=0.797 total time= 0.0s
[CV 2/5; 90/400] START max_depth=3, min_samples_leaf=1, n_estimators=10.........
[CV 2/5; 90/400] END max_depth=3, min_samples_leaf=1, n_estimators=10;, score=0.788 total time= 0.0s
[CV 3/5; 90/400] START max_depth=3, min_samples_leaf=1, n_estimators=10.........
[CV 3/5; 90/400] END max_depth=3, min_samples_leaf=1, n_estimators=10;, score=0.777 total time= 0.0s
[CV 4/5; 90/400] START max_depth=3, min_samples_leaf=1, n_estimators=10.........
[CV 4/5; 90/400] END max_depth=3, min_samples_leaf=1, n_estimators=10;, score=0.796 total time= 0.0s
[CV 5/5; 90/400] START max_depth=3, min_samples_leaf=1, n_estimators=10.........
[CV 5/5; 90/400] END max_depth=3, min_samples_leaf=1, n_estimators=10;, score=0.777 total time= 0.0s
[CV 1/5; 91/400] START max_depth=3, min_samples_leaf=2, n_estimators=1..........
[CV 1/5; 91/400] END max_depth=3, min_samples_leaf=2, n_estimators=1;, score=0.769 total time= 0.0s
[CV 2/5; 91/400] START max_depth=3, min_samples_leaf=2, n_estimators=1..........
[CV 2/5; 91/400] END max_depth=3, min_samples_leaf=2, n_estimators=1;, score=0.774 total time= 0.0s
[CV 3/5; 91/400] START max_depth=3, min_samples_leaf=2, n_estimators=1..........
[CV 3/5; 91/400] END max_depth=3, min_samples_leaf=2, n_estimators=1;, score=0.683 total time= 0.0s
[CV 4/5; 91/400] START max_depth=3, min_samples_leaf=2, n_estimators=1..........
[CV 4/5; 91/400] END max_depth=3, min_samples_leaf=2, n_estimators=1;, score=0.764 total time= 0.0s
[CV 5/5; 91/400] START max_depth=3, min_samples_leaf=2, n_estimators=1..........
[CV 5/5; 91/400] END max_depth=3, min_samples_leaf=2, n_estimators=1;, score=0.625 total time= 0.0s
[CV 1/5; 92/400] START max_depth=3, min_samples_leaf=2, n_estimators=2..........
[CV 1/5; 92/400] END max_depth=3, min_samples_leaf=2, n_estimators=2;, score=0.793 total time= 0.0s
[CV 2/5; 92/400] START max_depth=3, min_samples_leaf=2, n_estimators=2..........
[CV 2/5; 92/400] END max_depth=3, min_samples_leaf=2, n_estimators=2;, score=0.783 total time= 0.0s
[CV 3/5; 92/400] START max_depth=3, min_samples_leaf=2, n_estimators=2..........
[CV 3/5; 92/400] END max_depth=3, min_samples_leaf=2, n_estimators=2;, score=0.776 total time= 0.0s
[CV 4/5; 92/400] START max_depth=3, min_samples_leaf=2, n_estimators=2..........
[CV 4/5; 92/400] END max_depth=3, min_samples_leaf=2, n_estimators=2;, score=0.788 total time= 0.0s
[CV 5/5; 92/400] START max_depth=3, min_samples_leaf=2, n_estimators=2..........
[CV 5/5; 92/400] END max_depth=3, min_samples_leaf=2, n_estimators=2;, score=0.680 total time= 0.0s
[CV 1/5; 93/400] START max_depth=3, min_samples_leaf=2, n_estimators=3..........
[CV 1/5; 93/400] END max_depth=3, min_samples_leaf=2, n_estimators=3;, score=0.768 total time= 0.0s
[CV 2/5; 93/400] START max_depth=3, min_samples_leaf=2, n_estimators=3..........
[CV 2/5; 93/400] END max_depth=3, min_samples_leaf=2, n_estimators=3;, score=0.794 total time= 0.0s
[CV 3/5; 93/400] START max_depth=3, min_samples_leaf=2, n_estimators=3..........
[CV 3/5; 93/400] END max_depth=3, min_samples_leaf=2, n_estimators=3;, score=0.769 total time= 0.0s
[CV 4/5; 93/400] START max_depth=3, min_samples_leaf=2, n_estimators=3..........
[CV 4/5; 93/400] END max_depth=3, min_samples_leaf=2, n_estimators=3;, score=0.791 total time= 0.0s
[CV 5/5; 93/400] START max_depth=3, min_samples_leaf=2, n_estimators=3..........
[CV 5/5; 93/400] END max_depth=3, min_samples_leaf=2, n_estimators=3;, score=0.781 total time= 0.0s
[CV 1/5; 94/400] START max_depth=3, min_samples_leaf=2, n_estimators=4..........
[CV 1/5; 94/400] END max_depth=3, min_samples_leaf=2, n_estimators=4;, score=0.785 total time= 0.0s
[CV 2/5; 94/400] START max_depth=3, min_samples_leaf=2, n_estimators=4..........
[CV 2/5; 94/400] END max_depth=3, min_samples_leaf=2, n_estimators=4;, score=0.795 total time= 0.0s
[CV 3/5; 94/400] START max_depth=3, min_samples_leaf=2, n_estimators=4..........
[CV 3/5; 94/400] END max_depth=3, min_samples_leaf=2, n_estimators=4;, score=0.778 total time= 0.0s
[CV 4/5; 94/400] START max_depth=3, min_samples_leaf=2, n_estimators=4..........
[CV 4/5; 94/400] END max_depth=3, min_samples_leaf=2, n_estimators=4;, score=0.788 total time= 0.0s
[CV 5/5; 94/400] START max_depth=3, min_samples_leaf=2, n_estimators=4..........
[CV 5/5; 94/400] END max_depth=3, min_samples_leaf=2, n_estimators=4;, score=0.780 total time= 0.0s
[CV 1/5; 95/400] START max_depth=3, min_samples_leaf=2, n_estimators=5..........
[CV 1/5; 95/400] END max_depth=3, min_samples_leaf=2, n_estimators=5;, score=0.796 total time= 0.0s
[CV 2/5; 95/400] START max_depth=3, min_samples_leaf=2, n_estimators=5..........
[CV 2/5; 95/400] END max_depth=3, min_samples_leaf=2, n_estimators=5;, score=0.791 total time= 0.0s
[CV 3/5; 95/400] START max_depth=3, min_samples_leaf=2, n_estimators=5..........
[CV 3/5; 95/400] END max_depth=3, min_samples_leaf=2, n_estimators=5;, score=0.775 total time= 0.0s
[CV 4/5; 95/400] START max_depth=3, min_samples_leaf=2, n_estimators=5..........
[CV 4/5; 95/400] END max_depth=3, min_samples_leaf=2, n_estimators=5;, score=0.793 total time= 0.0s
[CV 5/5; 95/400] START max_depth=3, min_samples_leaf=2, n_estimators=5..........
[CV 5/5; 95/400] END max_depth=3, min_samples_leaf=2, n_estimators=5;, score=0.775 total time= 0.0s
[CV 1/5; 96/400] START max_depth=3, min_samples_leaf=2, n_estimators=6..........
[CV 1/5; 96/400] END max_depth=3, min_samples_leaf=2, n_estimators=6;, score=0.788 total time= 0.0s
[CV 2/5; 96/400] START max_depth=3, min_samples_leaf=2, n_estimators=6..........
[CV 2/5; 96/400] END max_depth=3, min_samples_leaf=2, n_estimators=6;, score=0.785 total time= 0.0s
[CV 3/5; 96/400] START max_depth=3, min_samples_leaf=2, n_estimators=6..........
[CV 3/5; 96/400] END max_depth=3, min_samples_leaf=2, n_estimators=6;, score=0.777 total time= 0.0s
[CV 4/5; 96/400] START max_depth=3, min_samples_leaf=2, n_estimators=6..........
[CV 4/5; 96/400] END max_depth=3, min_samples_leaf=2, n_estimators=6;, score=0.789 total time= 0.0s
[CV 5/5; 96/400] START max_depth=3, min_samples_leaf=2, n_estimators=6..........
[CV 5/5; 96/400] END max_depth=3, min_samples_leaf=2, n_estimators=6;, score=0.774 total time= 0.0s
[CV 1/5; 97/400] START max_depth=3, min_samples_leaf=2, n_estimators=7..........
[CV 1/5; 97/400] END max_depth=3, min_samples_leaf=2, n_estimators=7;, score=0.794 total time= 0.0s
[CV 2/5; 97/400] START max_depth=3, min_samples_leaf=2, n_estimators=7..........
[CV 2/5; 97/400] END max_depth=3, min_samples_leaf=2, n_estimators=7;, score=0.792 total time= 0.0s
[CV 3/5; 97/400] START max_depth=3, min_samples_leaf=2, n_estimators=7..........
[CV 3/5; 97/400] END max_depth=3, min_samples_leaf=2, n_estimators=7;, score=0.782 total time= 0.0s
[CV 4/5; 97/400] START max_depth=3, min_samples_leaf=2, n_estimators=7..........
[CV 4/5; 97/400] END max_depth=3, min_samples_leaf=2, n_estimators=7;, score=0.801 total time= 0.0s
[CV 5/5; 97/400] START max_depth=3, min_samples_leaf=2, n_estimators=7..........
[CV 5/5; 97/400] END max_depth=3, min_samples_leaf=2, n_estimators=7;, score=0.771 total time= 0.0s
[CV 1/5; 98/400] START max_depth=3, min_samples_leaf=2, n_estimators=8..........
[CV 1/5; 98/400] END max_depth=3, min_samples_leaf=2, n_estimators=8;, score=0.796 total time= 0.0s
[CV 2/5; 98/400] START max_depth=3, min_samples_leaf=2, n_estimators=8..........
[CV 2/5; 98/400] END max_depth=3, min_samples_leaf=2, n_estimators=8;, score=0.794 total time= 0.0s
[CV 3/5; 98/400] START max_depth=3, min_samples_leaf=2, n_estimators=8..........
[CV 3/5; 98/400] END max_depth=3, min_samples_leaf=2, n_estimators=8;, score=0.778 total time= 0.0s
[CV 4/5; 98/400] START max_depth=3, min_samples_leaf=2, n_estimators=8..........
[CV 4/5; 98/400] END max_depth=3, min_samples_leaf=2, n_estimators=8;, score=0.791 total time= 0.0s
[CV 5/5; 98/400] START max_depth=3, min_samples_leaf=2, n_estimators=8..........
[CV 5/5; 98/400] END max_depth=3, min_samples_leaf=2, n_estimators=8;, score=0.769 total time= 0.0s
[CV 1/5; 99/400] START max_depth=3, min_samples_leaf=2, n_estimators=9..........
[CV 1/5; 99/400] END max_depth=3, min_samples_leaf=2, n_estimators=9;, score=0.796 total time= 0.0s
[CV 2/5; 99/400] START max_depth=3, min_samples_leaf=2, n_estimators=9..........
[CV 2/5; 99/400] END max_depth=3, min_samples_leaf=2, n_estimators=9;, score=0.794 total time= 0.1s
[CV 3/5; 99/400] START max_depth=3, min_samples_leaf=2, n_estimators=9..........
[CV 3/5; 99/400] END max_depth=3, min_samples_leaf=2, n_estimators=9;, score=0.783 total time= 0.1s
[CV 4/5; 99/400] START max_depth=3, min_samples_leaf=2, n_estimators=9..........
[CV 4/5; 99/400] END max_depth=3, min_samples_leaf=2, n_estimators=9;, score=0.792 total time= 0.1s
[CV 5/5; 99/400] START max_depth=3, min_samples_leaf=2, n_estimators=9..........
[CV 5/5; 99/400] END max_depth=3, min_samples_leaf=2, n_estimators=9;, score=0.777 total time= 0.1s
[CV 1/5; 100/400] START max_depth=3, min_samples_leaf=2, n_estimators=10........
[CV 1/5; 100/400] END max_depth=3, min_samples_leaf=2, n_estimators=10;, score=0.799 total time= 0.1s
[CV 2/5; 100/400] START max_depth=3, min_samples_leaf=2, n_estimators=10........
[CV 2/5; 100/400] END max_depth=3, min_samples_leaf=2, n_estimators=10;, score=0.789 total time= 0.1s
[CV 3/5; 100/400] START max_depth=3, min_samples_leaf=2, n_estimators=10........
[CV 3/5; 100/400] END max_depth=3, min_samples_leaf=2, n_estimators=10;, score=0.778 total time= 0.1s
[CV 4/5; 100/400] START max_depth=3, min_samples_leaf=2, n_estimators=10........
[CV 4/5; 100/400] END max_depth=3, min_samples_leaf=2, n_estimators=10;, score=0.792 total time= 0.1s
[CV 5/5; 100/400] START max_depth=3, min_samples_leaf=2, n_estimators=10........
[CV 5/5; 100/400] END max_depth=3, min_samples_leaf=2, n_estimators=10;, score=0.778 total time= 0.1s
[CV 1/5; 101/400] START max_depth=3, min_samples_leaf=3, n_estimators=1.........
[CV 1/5; 101/400] END max_depth=3, min_samples_leaf=3, n_estimators=1;, score=0.641 total time= 0.0s
[CV 2/5; 101/400] START max_depth=3, min_samples_leaf=3, n_estimators=1.........
[CV 2/5; 101/400] END max_depth=3, min_samples_leaf=3, n_estimators=1;, score=0.736 total time= 0.0s
[CV 3/5; 101/400] START max_depth=3, min_samples_leaf=3, n_estimators=1.........
[CV 3/5; 101/400] END max_depth=3, min_samples_leaf=3, n_estimators=1;, score=0.654 total time= 0.0s
[CV 4/5; 101/400] START max_depth=3, min_samples_leaf=3, n_estimators=1.........
[CV 4/5; 101/400] END max_depth=3, min_samples_leaf=3, n_estimators=1;, score=0.626 total time= 0.0s
[CV 5/5; 101/400] START max_depth=3, min_samples_leaf=3, n_estimators=1.........
[CV 5/5; 101/400] END max_depth=3, min_samples_leaf=3, n_estimators=1;, score=0.769 total time= 0.0s
[CV 1/5; 102/400] START max_depth=3, min_samples_leaf=3, n_estimators=2.........
[CV 1/5; 102/400] END max_depth=3, min_samples_leaf=3, n_estimators=2;, score=0.666 total time= 0.0s
[CV 2/5; 102/400] START max_depth=3, min_samples_leaf=3, n_estimators=2.........
[CV 2/5; 102/400] END max_depth=3, min_samples_leaf=3, n_estimators=2;, score=0.793 total time= 0.0s
[CV 3/5; 102/400] START max_depth=3, min_samples_leaf=3, n_estimators=2.........
[CV 3/5; 102/400] END max_depth=3, min_samples_leaf=3, n_estimators=2;, score=0.786 total time= 0.0s
[CV 4/5; 102/400] START max_depth=3, min_samples_leaf=3, n_estimators=2.........
[CV 4/5; 102/400] END max_depth=3, min_samples_leaf=3, n_estimators=2;, score=0.775 total time= 0.0s
[CV 5/5; 102/400] START max_depth=3, min_samples_leaf=3, n_estimators=2.........
[CV 5/5; 102/400] END max_depth=3, min_samples_leaf=3, n_estimators=2;, score=0.762 total time= 0.0s
[CV 1/5; 103/400] START max_depth=3, min_samples_leaf=3, n_estimators=3.........
[CV 1/5; 103/400] END max_depth=3, min_samples_leaf=3, n_estimators=3;, score=0.792 total time= 0.0s
[CV 2/5; 103/400] START max_depth=3, min_samples_leaf=3, n_estimators=3.........
[CV 2/5; 103/400] END max_depth=3, min_samples_leaf=3, n_estimators=3;, score=0.785 total time= 0.0s
[CV 3/5; 103/400] START max_depth=3, min_samples_leaf=3, n_estimators=3.........
[CV 3/5; 103/400] END max_depth=3, min_samples_leaf=3, n_estimators=3;, score=0.776 total time= 0.0s
[CV 4/5; 103/400] START max_depth=3, min_samples_leaf=3, n_estimators=3.........
[CV 4/5; 103/400] END max_depth=3, min_samples_leaf=3, n_estimators=3;, score=0.793 total time= 0.0s
[CV 5/5; 103/400] START max_depth=3, min_samples_leaf=3, n_estimators=3.........
[CV 5/5; 103/400] END max_depth=3, min_samples_leaf=3, n_estimators=3;, score=0.780 total time= 0.0s
[CV 1/5; 104/400] START max_depth=3, min_samples_leaf=3, n_estimators=4.........
[CV 1/5; 104/400] END max_depth=3, min_samples_leaf=3, n_estimators=4;, score=0.796 total time= 0.0s
[CV 2/5; 104/400] START max_depth=3, min_samples_leaf=3, n_estimators=4.........
[CV 2/5; 104/400] END max_depth=3, min_samples_leaf=3, n_estimators=4;, score=0.788 total time= 0.0s
[CV 3/5; 104/400] START max_depth=3, min_samples_leaf=3, n_estimators=4.........
[CV 3/5; 104/400] END max_depth=3, min_samples_leaf=3, n_estimators=4;, score=0.781 total time= 0.0s
[CV 4/5; 104/400] START max_depth=3, min_samples_leaf=3, n_estimators=4.........
[CV 4/5; 104/400] END max_depth=3, min_samples_leaf=3, n_estimators=4;, score=0.789 total time= 0.0s
[CV 5/5; 104/400] START max_depth=3, min_samples_leaf=3, n_estimators=4.........
[CV 5/5; 104/400] END max_depth=3, min_samples_leaf=3, n_estimators=4;, score=0.780 total time= 0.0s
[CV 1/5; 105/400] START max_depth=3, min_samples_leaf=3, n_estimators=5.........
[CV 1/5; 105/400] END max_depth=3, min_samples_leaf=3, n_estimators=5;, score=0.798 total time= 0.0s
[CV 2/5; 105/400] START max_depth=3, min_samples_leaf=3, n_estimators=5.........
[CV 2/5; 105/400] END max_depth=3, min_samples_leaf=3, n_estimators=5;, score=0.790 total time= 0.0s
[CV 3/5; 105/400] START max_depth=3, min_samples_leaf=3, n_estimators=5.........
[CV 3/5; 105/400] END max_depth=3, min_samples_leaf=3, n_estimators=5;, score=0.786 total time= 0.0s
[CV 4/5; 105/400] START max_depth=3, min_samples_leaf=3, n_estimators=5.........
[CV 4/5; 105/400] END max_depth=3, min_samples_leaf=3, n_estimators=5;, score=0.794 total time= 0.0s
[CV 5/5; 105/400] START max_depth=3, min_samples_leaf=3, n_estimators=5.........
[CV 5/5; 105/400] END max_depth=3, min_samples_leaf=3, n_estimators=5;, score=0.775 total time= 0.0s
[CV 1/5; 106/400] START max_depth=3, min_samples_leaf=3, n_estimators=6.........
[CV 1/5; 106/400] END max_depth=3, min_samples_leaf=3, n_estimators=6;, score=0.798 total time= 0.0s
[CV 2/5; 106/400] START max_depth=3, min_samples_leaf=3, n_estimators=6.........
[CV 2/5; 106/400] END max_depth=3, min_samples_leaf=3, n_estimators=6;, score=0.796 total time= 0.0s
[CV 3/5; 106/400] START max_depth=3, min_samples_leaf=3, n_estimators=6.........
[CV 3/5; 106/400] END max_depth=3, min_samples_leaf=3, n_estimators=6;, score=0.783 total time= 0.0s
[CV 4/5; 106/400] START max_depth=3, min_samples_leaf=3, n_estimators=6.........
[CV 4/5; 106/400] END max_depth=3, min_samples_leaf=3, n_estimators=6;, score=0.792 total time= 0.0s
[CV 5/5; 106/400] START max_depth=3, min_samples_leaf=3, n_estimators=6.........
[CV 5/5; 106/400] END max_depth=3, min_samples_leaf=3, n_estimators=6;, score=0.778 total time= 0.0s
[CV 1/5; 107/400] START max_depth=3, min_samples_leaf=3, n_estimators=7.........
[CV 1/5; 107/400] END max_depth=3, min_samples_leaf=3, n_estimators=7;, score=0.781 total time= 0.0s
[CV 2/5; 107/400] START max_depth=3, min_samples_leaf=3, n_estimators=7.........
[CV 2/5; 107/400] END max_depth=3, min_samples_leaf=3, n_estimators=7;, score=0.785 total time= 0.0s
[CV 3/5; 107/400] START max_depth=3, min_samples_leaf=3, n_estimators=7.........
[CV 3/5; 107/400] END max_depth=3, min_samples_leaf=3, n_estimators=7;, score=0.783 total time= 0.0s
[CV 4/5; 107/400] START max_depth=3, min_samples_leaf=3, n_estimators=7.........
[CV 4/5; 107/400] END max_depth=3, min_samples_leaf=3, n_estimators=7;, score=0.792 total time= 0.0s
[CV 5/5; 107/400] START max_depth=3, min_samples_leaf=3, n_estimators=7.........
[CV 5/5; 107/400] END max_depth=3, min_samples_leaf=3, n_estimators=7;, score=0.775 total time= 0.1s
[CV 1/5; 108/400] START max_depth=3, min_samples_leaf=3, n_estimators=8.........
[CV 1/5; 108/400] END max_depth=3, min_samples_leaf=3, n_estimators=8;, score=0.785 total time= 0.1s
[CV 2/5; 108/400] START max_depth=3, min_samples_leaf=3, n_estimators=8.........
[CV 2/5; 108/400] END max_depth=3, min_samples_leaf=3, n_estimators=8;, score=0.786 total time= 0.1s
[CV 3/5; 108/400] START max_depth=3, min_samples_leaf=3, n_estimators=8.........
[CV 3/5; 108/400] END max_depth=3, min_samples_leaf=3, n_estimators=8;, score=0.782 total time= 0.1s
[CV 4/5; 108/400] START max_depth=3, min_samples_leaf=3, n_estimators=8.........
[CV 4/5; 108/400] END max_depth=3, min_samples_leaf=3, n_estimators=8;, score=0.791 total time= 0.1s
[CV 5/5; 108/400] START max_depth=3, min_samples_leaf=3, n_estimators=8.........
[CV 5/5; 108/400] END max_depth=3, min_samples_leaf=3, n_estimators=8;, score=0.782 total time= 0.1s
[CV 1/5; 109/400] START max_depth=3, min_samples_leaf=3, n_estimators=9.........
[CV 1/5; 109/400] END max_depth=3, min_samples_leaf=3, n_estimators=9;, score=0.799 total time= 0.1s
[CV 2/5; 109/400] START max_depth=3, min_samples_leaf=3, n_estimators=9.........
[CV 2/5; 109/400] END max_depth=3, min_samples_leaf=3, n_estimators=9;, score=0.788 total time= 0.1s
[CV 3/5; 109/400] START max_depth=3, min_samples_leaf=3, n_estimators=9.........
[CV 3/5; 109/400] END max_depth=3, min_samples_leaf=3, n_estimators=9;, score=0.778 total time= 0.1s
[CV 4/5; 109/400] START max_depth=3, min_samples_leaf=3, n_estimators=9.........
[CV 4/5; 109/400] END max_depth=3, min_samples_leaf=3, n_estimators=9;, score=0.791 total time= 0.1s
[CV 5/5; 109/400] START max_depth=3, min_samples_leaf=3, n_estimators=9.........
[CV 5/5; 109/400] END max_depth=3, min_samples_leaf=3, n_estimators=9;, score=0.774 total time= 0.1s
[CV 1/5; 110/400] START max_depth=3, min_samples_leaf=3, n_estimators=10........
[CV 1/5; 110/400] END max_depth=3, min_samples_leaf=3, n_estimators=10;, score=0.776 total time= 0.1s
[CV 2/5; 110/400] START max_depth=3, min_samples_leaf=3, n_estimators=10........
[CV 2/5; 110/400] END max_depth=3, min_samples_leaf=3, n_estimators=10;, score=0.788 total time= 0.1s
[CV 3/5; 110/400] START max_depth=3, min_samples_leaf=3, n_estimators=10........
[CV 3/5; 110/400] END max_depth=3, min_samples_leaf=3, n_estimators=10;, score=0.778 total time= 0.1s
[CV 4/5; 110/400] START max_depth=3, min_samples_leaf=3, n_estimators=10........
[CV 4/5; 110/400] END max_depth=3, min_samples_leaf=3, n_estimators=10;, score=0.792 total time= 0.1s
[CV 5/5; 110/400] START max_depth=3, min_samples_leaf=3, n_estimators=10........
[CV 5/5; 110/400] END max_depth=3, min_samples_leaf=3, n_estimators=10;, score=0.776 total time= 0.1s
[CV 1/5; 111/400] START max_depth=3, min_samples_leaf=4, n_estimators=1.........
[CV 1/5; 111/400] END max_depth=3, min_samples_leaf=4, n_estimators=1;, score=0.802 total time= 0.0s
[CV 2/5; 111/400] START max_depth=3, min_samples_leaf=4, n_estimators=1.........
[CV 2/5; 111/400] END max_depth=3, min_samples_leaf=4, n_estimators=1;, score=0.799 total time= 0.0s
[CV 3/5; 111/400] START max_depth=3, min_samples_leaf=4, n_estimators=1.........
[CV 3/5; 111/400] END max_depth=3, min_samples_leaf=4, n_estimators=1;, score=0.752 total time= 0.0s
[CV 4/5; 111/400] START max_depth=3, min_samples_leaf=4, n_estimators=1.........
[CV 4/5; 111/400] END max_depth=3, min_samples_leaf=4, n_estimators=1;, score=0.779 total time= 0.0s
[CV 5/5; 111/400] START max_depth=3, min_samples_leaf=4, n_estimators=1.........
[CV 5/5; 111/400] END max_depth=3, min_samples_leaf=4, n_estimators=1;, score=0.590 total time= 0.0s
[CV 1/5; 112/400] START max_depth=3, min_samples_leaf=4, n_estimators=2.........
[CV 1/5; 112/400] END max_depth=3, min_samples_leaf=4, n_estimators=2;, score=0.794 total time= 0.0s
[CV 2/5; 112/400] START max_depth=3, min_samples_leaf=4, n_estimators=2.........
[CV 2/5; 112/400] END max_depth=3, min_samples_leaf=4, n_estimators=2;, score=0.795 total time= 0.0s
[CV 3/5; 112/400] START max_depth=3, min_samples_leaf=4, n_estimators=2.........
[CV 3/5; 112/400] END max_depth=3, min_samples_leaf=4, n_estimators=2;, score=0.757 total time= 0.0s
[CV 4/5; 112/400] START max_depth=3, min_samples_leaf=4, n_estimators=2.........
[CV 4/5; 112/400] END max_depth=3, min_samples_leaf=4, n_estimators=2;, score=0.785 total time= 0.0s
[CV 5/5; 112/400] START max_depth=3, min_samples_leaf=4, n_estimators=2.........
[CV 5/5; 112/400] END max_depth=3, min_samples_leaf=4, n_estimators=2;, score=0.774 total time= 0.0s
[CV 1/5; 113/400] START max_depth=3, min_samples_leaf=4, n_estimators=3.........
[CV 1/5; 113/400] END max_depth=3, min_samples_leaf=4, n_estimators=3;, score=0.776 total time= 0.0s
[CV 2/5; 113/400] START max_depth=3, min_samples_leaf=4, n_estimators=3.........
[CV 2/5; 113/400] END max_depth=3, min_samples_leaf=4, n_estimators=3;, score=0.793 total time= 0.0s
[CV 3/5; 113/400] START max_depth=3, min_samples_leaf=4, n_estimators=3.........
[CV 3/5; 113/400] END max_depth=3, min_samples_leaf=4, n_estimators=3;, score=0.782 total time= 0.0s
[CV 4/5; 113/400] START max_depth=3, min_samples_leaf=4, n_estimators=3.........
[CV 4/5; 113/400] END max_depth=3, min_samples_leaf=4, n_estimators=3;, score=0.783 total time= 0.0s
[CV 5/5; 113/400] START max_depth=3, min_samples_leaf=4, n_estimators=3.........
[CV 5/5; 113/400] END max_depth=3, min_samples_leaf=4, n_estimators=3;, score=0.684 total time= 0.0s
[CV 1/5; 114/400] START max_depth=3, min_samples_leaf=4, n_estimators=4.........
[CV 1/5; 114/400] END max_depth=3, min_samples_leaf=4, n_estimators=4;, score=0.799 total time= 0.0s
[CV 2/5; 114/400] START max_depth=3, min_samples_leaf=4, n_estimators=4.........
[CV 2/5; 114/400] END max_depth=3, min_samples_leaf=4, n_estimators=4;, score=0.781 total time= 0.0s
[CV 3/5; 114/400] START max_depth=3, min_samples_leaf=4, n_estimators=4.........
[CV 3/5; 114/400] END max_depth=3, min_samples_leaf=4, n_estimators=4;, score=0.780 total time= 0.0s
[CV 4/5; 114/400] START max_depth=3, min_samples_leaf=4, n_estimators=4.........
[CV 4/5; 114/400] END max_depth=3, min_samples_leaf=4, n_estimators=4;, score=0.793 total time= 0.0s
[CV 5/5; 114/400] START max_depth=3, min_samples_leaf=4, n_estimators=4.........
[CV 5/5; 114/400] END max_depth=3, min_samples_leaf=4, n_estimators=4;, score=0.775 total time= 0.0s
[CV 1/5; 115/400] START max_depth=3, min_samples_leaf=4, n_estimators=5.........
[CV 1/5; 115/400] END max_depth=3, min_samples_leaf=4, n_estimators=5;, score=0.791 total time= 0.0s
[CV 2/5; 115/400] START max_depth=3, min_samples_leaf=4, n_estimators=5.........
[CV 2/5; 115/400] END max_depth=3, min_samples_leaf=4, n_estimators=5;, score=0.794 total time= 0.0s
[CV 3/5; 115/400] START max_depth=3, min_samples_leaf=4, n_estimators=5.........
[CV 3/5; 115/400] END max_depth=3, min_samples_leaf=4, n_estimators=5;, score=0.787 total time= 0.0s
[CV 4/5; 115/400] START max_depth=3, min_samples_leaf=4, n_estimators=5.........
[CV 4/5; 115/400] END max_depth=3, min_samples_leaf=4, n_estimators=5;, score=0.785 total time= 0.0s
[CV 5/5; 115/400] START max_depth=3, min_samples_leaf=4, n_estimators=5.........
[CV 5/5; 115/400] END max_depth=3, min_samples_leaf=4, n_estimators=5;, score=0.769 total time= 0.0s
[CV 1/5; 116/400] START max_depth=3, min_samples_leaf=4, n_estimators=6.........
[CV 1/5; 116/400] END max_depth=3, min_samples_leaf=4, n_estimators=6;, score=0.795 total time= 0.0s
[CV 2/5; 116/400] START max_depth=3, min_samples_leaf=4, n_estimators=6.........
[CV 2/5; 116/400] END max_depth=3, min_samples_leaf=4, n_estimators=6;, score=0.792 total time= 0.0s
[CV 3/5; 116/400] START max_depth=3, min_samples_leaf=4, n_estimators=6.........
[CV 3/5; 116/400] END max_depth=3, min_samples_leaf=4, n_estimators=6;, score=0.778 total time= 0.0s
[CV 4/5; 116/400] START max_depth=3, min_samples_leaf=4, n_estimators=6.........
[CV 4/5; 116/400] END max_depth=3, min_samples_leaf=4, n_estimators=6;, score=0.791 total time= 0.0s
[CV 5/5; 116/400] START max_depth=3, min_samples_leaf=4, n_estimators=6.........
[CV 5/5; 116/400] END max_depth=3, min_samples_leaf=4, n_estimators=6;, score=0.769 total time= 0.0s
[CV 1/5; 117/400] START max_depth=3, min_samples_leaf=4, n_estimators=7.........
[CV 1/5; 117/400] END max_depth=3, min_samples_leaf=4, n_estimators=7;, score=0.792 total time= 0.0s
[CV 2/5; 117/400] START max_depth=3, min_samples_leaf=4, n_estimators=7.........
[CV 2/5; 117/400] END max_depth=3, min_samples_leaf=4, n_estimators=7;, score=0.788 total time= 0.0s
[CV 3/5; 117/400] START max_depth=3, min_samples_leaf=4, n_estimators=7.........
[CV 3/5; 117/400] END max_depth=3, min_samples_leaf=4, n_estimators=7;, score=0.780 total time= 0.0s
[CV 4/5; 117/400] START max_depth=3, min_samples_leaf=4, n_estimators=7.........
[CV 4/5; 117/400] END max_depth=3, min_samples_leaf=4, n_estimators=7;, score=0.790 total time= 0.0s
[CV 5/5; 117/400] START max_depth=3, min_samples_leaf=4, n_estimators=7.........
[CV 5/5; 117/400] END max_depth=3, min_samples_leaf=4, n_estimators=7;, score=0.772 total time= 0.0s
[CV 1/5; 118/400] START max_depth=3, min_samples_leaf=4, n_estimators=8.........
[CV 1/5; 118/400] END max_depth=3, min_samples_leaf=4, n_estimators=8;, score=0.796 total time= 0.0s
[CV 2/5; 118/400] START max_depth=3, min_samples_leaf=4, n_estimators=8.........
[CV 2/5; 118/400] END max_depth=3, min_samples_leaf=4, n_estimators=8;, score=0.789 total time= 0.0s
[CV 3/5; 118/400] START max_depth=3, min_samples_leaf=4, n_estimators=8.........
[CV 3/5; 118/400] END max_depth=3, min_samples_leaf=4, n_estimators=8;, score=0.780 total time= 0.0s
[CV 4/5; 118/400] START max_depth=3, min_samples_leaf=4, n_estimators=8.........
[CV 4/5; 118/400] END max_depth=3, min_samples_leaf=4, n_estimators=8;, score=0.799 total time= 0.0s
[CV 5/5; 118/400] START max_depth=3, min_samples_leaf=4, n_estimators=8.........
[CV 5/5; 118/400] END max_depth=3, min_samples_leaf=4, n_estimators=8;, score=0.777 total time= 0.0s
[CV 1/5; 119/400] START max_depth=3, min_samples_leaf=4, n_estimators=9.........
[CV 1/5; 119/400] END max_depth=3, min_samples_leaf=4, n_estimators=9;, score=0.787 total time= 0.0s
[CV 2/5; 119/400] START max_depth=3, min_samples_leaf=4, n_estimators=9.........
[CV 2/5; 119/400] END max_depth=3, min_samples_leaf=4, n_estimators=9;, score=0.791 total time= 0.0s
[CV 3/5; 119/400] START max_depth=3, min_samples_leaf=4, n_estimators=9.........
[CV 3/5; 119/400] END max_depth=3, min_samples_leaf=4, n_estimators=9;, score=0.776 total time= 0.0s
[CV 4/5; 119/400] START max_depth=3, min_samples_leaf=4, n_estimators=9.........
[CV 4/5; 119/400] END max_depth=3, min_samples_leaf=4, n_estimators=9;, score=0.794 total time= 0.0s
[CV 5/5; 119/400] START max_depth=3, min_samples_leaf=4, n_estimators=9.........
[CV 5/5; 119/400] END max_depth=3, min_samples_leaf=4, n_estimators=9;, score=0.771 total time= 0.0s
[CV 1/5; 120/400] START max_depth=3, min_samples_leaf=4, n_estimators=10........
[CV 1/5; 120/400] END max_depth=3, min_samples_leaf=4, n_estimators=10;, score=0.790 total time= 0.0s
[CV 2/5; 120/400] START max_depth=3, min_samples_leaf=4, n_estimators=10........
[CV 2/5; 120/400] END max_depth=3, min_samples_leaf=4, n_estimators=10;, score=0.791 total time= 0.0s
[CV 3/5; 120/400] START max_depth=3, min_samples_leaf=4, n_estimators=10........
[CV 3/5; 120/400] END max_depth=3, min_samples_leaf=4, n_estimators=10;, score=0.780 total time= 0.0s
[CV 4/5; 120/400] START max_depth=3, min_samples_leaf=4, n_estimators=10........
[CV 4/5; 120/400] END max_depth=3, min_samples_leaf=4, n_estimators=10;, score=0.791 total time= 0.0s
[CV 5/5; 120/400] START max_depth=3, min_samples_leaf=4, n_estimators=10........
[CV 5/5; 120/400] END max_depth=3, min_samples_leaf=4, n_estimators=10;, score=0.770 total time= 0.0s
[CV 1/5; 121/400] START max_depth=4, min_samples_leaf=1, n_estimators=1.........
[CV 1/5; 121/400] END max_depth=4, min_samples_leaf=1, n_estimators=1;, score=0.776 total time= 0.0s
[CV 2/5; 121/400] START max_depth=4, min_samples_leaf=1, n_estimators=1.........
[CV 2/5; 121/400] END max_depth=4, min_samples_leaf=1, n_estimators=1;, score=0.791 total time= 0.0s
[CV 3/5; 121/400] START max_depth=4, min_samples_leaf=1, n_estimators=1.........
[CV 3/5; 121/400] END max_depth=4, min_samples_leaf=1, n_estimators=1;, score=0.785 total time= 0.0s
[CV 4/5; 121/400] START max_depth=4, min_samples_leaf=1, n_estimators=1.........
[CV 4/5; 121/400] END max_depth=4, min_samples_leaf=1, n_estimators=1;, score=0.659 total time= 0.0s
[CV 5/5; 121/400] START max_depth=4, min_samples_leaf=1, n_estimators=1.........
[CV 5/5; 121/400] END max_depth=4, min_samples_leaf=1, n_estimators=1;, score=0.760 total time= 0.0s
[CV 1/5; 122/400] START max_depth=4, min_samples_leaf=1, n_estimators=2.........
[CV 1/5; 122/400] END max_depth=4, min_samples_leaf=1, n_estimators=2;, score=0.796 total time= 0.0s
[CV 2/5; 122/400] START max_depth=4, min_samples_leaf=1, n_estimators=2.........
[CV 2/5; 122/400] END max_depth=4, min_samples_leaf=1, n_estimators=2;, score=0.791 total time= 0.0s
[CV 3/5; 122/400] START max_depth=4, min_samples_leaf=1, n_estimators=2.........
[CV 3/5; 122/400] END max_depth=4, min_samples_leaf=1, n_estimators=2;, score=0.797 total time= 0.0s
[CV 4/5; 122/400] START max_depth=4, min_samples_leaf=1, n_estimators=2.........
[CV 4/5; 122/400] END max_depth=4, min_samples_leaf=1, n_estimators=2;, score=0.788 total time= 0.0s
[CV 5/5; 122/400] START max_depth=4, min_samples_leaf=1, n_estimators=2.........
[CV 5/5; 122/400] END max_depth=4, min_samples_leaf=1, n_estimators=2;, score=0.786 total time= 0.0s
[CV 1/5; 123/400] START max_depth=4, min_samples_leaf=1, n_estimators=3.........
[CV 1/5; 123/400] END max_depth=4, min_samples_leaf=1, n_estimators=3;, score=0.792 total time= 0.0s
[CV 2/5; 123/400] START max_depth=4, min_samples_leaf=1, n_estimators=3.........
[CV 2/5; 123/400] END max_depth=4, min_samples_leaf=1, n_estimators=3;, score=0.793 total time= 0.0s
[CV 3/5; 123/400] START max_depth=4, min_samples_leaf=1, n_estimators=3.........
[CV 3/5; 123/400] END max_depth=4, min_samples_leaf=1, n_estimators=3;, score=0.783 total time= 0.0s
[CV 4/5; 123/400] START max_depth=4, min_samples_leaf=1, n_estimators=3.........
[CV 4/5; 123/400] END max_depth=4, min_samples_leaf=1, n_estimators=3;, score=0.793 total time= 0.0s
[CV 5/5; 123/400] START max_depth=4, min_samples_leaf=1, n_estimators=3.........
[CV 5/5; 123/400] END max_depth=4, min_samples_leaf=1, n_estimators=3;, score=0.785 total time= 0.0s
[CV 1/5; 124/400] START max_depth=4, min_samples_leaf=1, n_estimators=4.........
[CV 1/5; 124/400] END max_depth=4, min_samples_leaf=1, n_estimators=4;, score=0.805 total time= 0.0s
[CV 2/5; 124/400] START max_depth=4, min_samples_leaf=1, n_estimators=4.........
[CV 2/5; 124/400] END max_depth=4, min_samples_leaf=1, n_estimators=4;, score=0.793 total time= 0.0s
[CV 3/5; 124/400] START max_depth=4, min_samples_leaf=1, n_estimators=4.........
[CV 3/5; 124/400] END max_depth=4, min_samples_leaf=1, n_estimators=4;, score=0.780 total time= 0.0s
[CV 4/5; 124/400] START max_depth=4, min_samples_leaf=1, n_estimators=4.........
[CV 4/5; 124/400] END max_depth=4, min_samples_leaf=1, n_estimators=4;, score=0.796 total time= 0.0s
[CV 5/5; 124/400] START max_depth=4, min_samples_leaf=1, n_estimators=4.........
[CV 5/5; 124/400] END max_depth=4, min_samples_leaf=1, n_estimators=4;, score=0.786 total time= 0.0s
[CV 1/5; 125/400] START max_depth=4, min_samples_leaf=1, n_estimators=5.........
[CV 1/5; 125/400] END max_depth=4, min_samples_leaf=1, n_estimators=5;, score=0.811 total time= 0.0s
[CV 2/5; 125/400] START max_depth=4, min_samples_leaf=1, n_estimators=5.........
[CV 2/5; 125/400] END max_depth=4, min_samples_leaf=1, n_estimators=5;, score=0.791 total time= 0.0s
[CV 3/5; 125/400] START max_depth=4, min_samples_leaf=1, n_estimators=5.........
[CV 3/5; 125/400] END max_depth=4, min_samples_leaf=1, n_estimators=5;, score=0.785 total time= 0.0s
[CV 4/5; 125/400] START max_depth=4, min_samples_leaf=1, n_estimators=5.........
[CV 4/5; 125/400] END max_depth=4, min_samples_leaf=1, n_estimators=5;, score=0.802 total time= 0.0s
[CV 5/5; 125/400] START max_depth=4, min_samples_leaf=1, n_estimators=5.........
[CV 5/5; 125/400] END max_depth=4, min_samples_leaf=1, n_estimators=5;, score=0.771 total time= 0.0s
[CV 1/5; 126/400] START max_depth=4, min_samples_leaf=1, n_estimators=6.........
[CV 1/5; 126/400] END max_depth=4, min_samples_leaf=1, n_estimators=6;, score=0.802 total time= 0.0s
[CV 2/5; 126/400] START max_depth=4, min_samples_leaf=1, n_estimators=6.........
[CV 2/5; 126/400] END max_depth=4, min_samples_leaf=1, n_estimators=6;, score=0.795 total time= 0.0s
[CV 3/5; 126/400] START max_depth=4, min_samples_leaf=1, n_estimators=6.........
[CV 3/5; 126/400] END max_depth=4, min_samples_leaf=1, n_estimators=6;, score=0.788 total time= 0.0s
[CV 4/5; 126/400] START max_depth=4, min_samples_leaf=1, n_estimators=6.........
[CV 4/5; 126/400] END max_depth=4, min_samples_leaf=1, n_estimators=6;, score=0.799 total time= 0.0s
[CV 5/5; 126/400] START max_depth=4, min_samples_leaf=1, n_estimators=6.........
[CV 5/5; 126/400] END max_depth=4, min_samples_leaf=1, n_estimators=6;, score=0.775 total time= 0.0s
[CV 1/5; 127/400] START max_depth=4, min_samples_leaf=1, n_estimators=7.........
[CV 1/5; 127/400] END max_depth=4, min_samples_leaf=1, n_estimators=7;, score=0.798 total time= 0.0s
[CV 2/5; 127/400] START max_depth=4, min_samples_leaf=1, n_estimators=7.........
[CV 2/5; 127/400] END max_depth=4, min_samples_leaf=1, n_estimators=7;, score=0.796 total time= 0.0s
[CV 3/5; 127/400] START max_depth=4, min_samples_leaf=1, n_estimators=7.........
[CV 3/5; 127/400] END max_depth=4, min_samples_leaf=1, n_estimators=7;, score=0.785 total time= 0.0s
[CV 4/5; 127/400] START max_depth=4, min_samples_leaf=1, n_estimators=7.........
[CV 4/5; 127/400] END max_depth=4, min_samples_leaf=1, n_estimators=7;, score=0.794 total time= 0.0s
[CV 5/5; 127/400] START max_depth=4, min_samples_leaf=1, n_estimators=7.........
[CV 5/5; 127/400] END max_depth=4, min_samples_leaf=1, n_estimators=7;, score=0.783 total time= 0.0s
[CV 1/5; 128/400] START max_depth=4, min_samples_leaf=1, n_estimators=8.........
[CV 1/5; 128/400] END max_depth=4, min_samples_leaf=1, n_estimators=8;, score=0.799 total time= 0.0s
[CV 2/5; 128/400] START max_depth=4, min_samples_leaf=1, n_estimators=8.........
[CV 2/5; 128/400] END max_depth=4, min_samples_leaf=1, n_estimators=8;, score=0.790 total time= 0.0s
[CV 3/5; 128/400] START max_depth=4, min_samples_leaf=1, n_estimators=8.........
[CV 3/5; 128/400] END max_depth=4, min_samples_leaf=1, n_estimators=8;, score=0.787 total time= 0.0s
[CV 4/5; 128/400] START max_depth=4, min_samples_leaf=1, n_estimators=8.........
[CV 4/5; 128/400] END max_depth=4, min_samples_leaf=1, n_estimators=8;, score=0.796 total time= 0.0s
[CV 5/5; 128/400] START max_depth=4, min_samples_leaf=1, n_estimators=8.........
[CV 5/5; 128/400] END max_depth=4, min_samples_leaf=1, n_estimators=8;, score=0.777 total time= 0.0s
[CV 1/5; 129/400] START max_depth=4, min_samples_leaf=1, n_estimators=9.........
[CV 1/5; 129/400] END max_depth=4, min_samples_leaf=1, n_estimators=9;, score=0.794 total time= 0.0s
[CV 2/5; 129/400] START max_depth=4, min_samples_leaf=1, n_estimators=9.........
[CV 2/5; 129/400] END max_depth=4, min_samples_leaf=1, n_estimators=9;, score=0.796 total time= 0.0s
[CV 3/5; 129/400] START max_depth=4, min_samples_leaf=1, n_estimators=9.........
[CV 3/5; 129/400] END max_depth=4, min_samples_leaf=1, n_estimators=9;, score=0.780 total time= 0.0s
[CV 4/5; 129/400] START max_depth=4, min_samples_leaf=1, n_estimators=9.........
[CV 4/5; 129/400] END max_depth=4, min_samples_leaf=1, n_estimators=9;, score=0.799 total time= 0.1s
[CV 5/5; 129/400] START max_depth=4, min_samples_leaf=1, n_estimators=9.........
[CV 5/5; 129/400] END max_depth=4, min_samples_leaf=1, n_estimators=9;, score=0.775 total time= 0.1s
[CV 1/5; 130/400] START max_depth=4, min_samples_leaf=1, n_estimators=10........
[CV 1/5; 130/400] END max_depth=4, min_samples_leaf=1, n_estimators=10;, score=0.804 total time= 0.1s
[CV 2/5; 130/400] START max_depth=4, min_samples_leaf=1, n_estimators=10........
[CV 2/5; 130/400] END max_depth=4, min_samples_leaf=1, n_estimators=10;, score=0.794 total time= 0.1s
[CV 3/5; 130/400] START max_depth=4, min_samples_leaf=1, n_estimators=10........
[CV 3/5; 130/400] END max_depth=4, min_samples_leaf=1, n_estimators=10;, score=0.791 total time= 0.1s
[CV 4/5; 130/400] START max_depth=4, min_samples_leaf=1, n_estimators=10........
[CV 4/5; 130/400] END max_depth=4, min_samples_leaf=1, n_estimators=10;, score=0.798 total time= 0.0s
[CV 5/5; 130/400] START max_depth=4, min_samples_leaf=1, n_estimators=10........
[CV 5/5; 130/400] END max_depth=4, min_samples_leaf=1, n_estimators=10;, score=0.781 total time= 0.1s
[CV 1/5; 131/400] START max_depth=4, min_samples_leaf=2, n_estimators=1.........
[CV 1/5; 131/400] END max_depth=4, min_samples_leaf=2, n_estimators=1;, score=0.795 total time= 0.0s
[CV 2/5; 131/400] START max_depth=4, min_samples_leaf=2, n_estimators=1.........
[CV 2/5; 131/400] END max_depth=4, min_samples_leaf=2, n_estimators=1;, score=0.794 total time= 0.0s
[CV 3/5; 131/400] START max_depth=4, min_samples_leaf=2, n_estimators=1.........
[CV 3/5; 131/400] END max_depth=4, min_samples_leaf=2, n_estimators=1;, score=0.788 total time= 0.0s
[CV 4/5; 131/400] START max_depth=4, min_samples_leaf=2, n_estimators=1.........
[CV 4/5; 131/400] END max_depth=4, min_samples_leaf=2, n_estimators=1;, score=0.801 total time= 0.0s
[CV 5/5; 131/400] START max_depth=4, min_samples_leaf=2, n_estimators=1.........
[CV 5/5; 131/400] END max_depth=4, min_samples_leaf=2, n_estimators=1;, score=0.755 total time= 0.0s
[CV 1/5; 132/400] START max_depth=4, min_samples_leaf=2, n_estimators=2.........
[CV 1/5; 132/400] END max_depth=4, min_samples_leaf=2, n_estimators=2;, score=0.814 total time= 0.0s
[CV 2/5; 132/400] START max_depth=4, min_samples_leaf=2, n_estimators=2.........
[CV 2/5; 132/400] END max_depth=4, min_samples_leaf=2, n_estimators=2;, score=0.793 total time= 0.0s
[CV 3/5; 132/400] START max_depth=4, min_samples_leaf=2, n_estimators=2.........
[CV 3/5; 132/400] END max_depth=4, min_samples_leaf=2, n_estimators=2;, score=0.779 total time= 0.0s
[CV 4/5; 132/400] START max_depth=4, min_samples_leaf=2, n_estimators=2.........
[CV 4/5; 132/400] END max_depth=4, min_samples_leaf=2, n_estimators=2;, score=0.787 total time= 0.0s
[CV 5/5; 132/400] START max_depth=4, min_samples_leaf=2, n_estimators=2.........
[CV 5/5; 132/400] END max_depth=4, min_samples_leaf=2, n_estimators=2;, score=0.790 total time= 0.0s
[CV 1/5; 133/400] START max_depth=4, min_samples_leaf=2, n_estimators=3.........
[CV 1/5; 133/400] END max_depth=4, min_samples_leaf=2, n_estimators=3;, score=0.803 total time= 0.0s
[CV 2/5; 133/400] START max_depth=4, min_samples_leaf=2, n_estimators=3.........
[CV 2/5; 133/400] END max_depth=4, min_samples_leaf=2, n_estimators=3;, score=0.794 total time= 0.0s
[CV 3/5; 133/400] START max_depth=4, min_samples_leaf=2, n_estimators=3.........
[CV 3/5; 133/400] END max_depth=4, min_samples_leaf=2, n_estimators=3;, score=0.783 total time= 0.0s
[CV 4/5; 133/400] START max_depth=4, min_samples_leaf=2, n_estimators=3.........
[CV 4/5; 133/400] END max_depth=4, min_samples_leaf=2, n_estimators=3;, score=0.795 total time= 0.0s
[CV 5/5; 133/400] START max_depth=4, min_samples_leaf=2, n_estimators=3.........
[CV 5/5; 133/400] END max_depth=4, min_samples_leaf=2, n_estimators=3;, score=0.792 total time= 0.0s
[CV 1/5; 134/400] START max_depth=4, min_samples_leaf=2, n_estimators=4.........
[CV 1/5; 134/400] END max_depth=4, min_samples_leaf=2, n_estimators=4;, score=0.799 total time= 0.0s
[CV 2/5; 134/400] START max_depth=4, min_samples_leaf=2, n_estimators=4.........
[CV 2/5; 134/400] END max_depth=4, min_samples_leaf=2, n_estimators=4;, score=0.758 total time= 0.0s
[CV 3/5; 134/400] START max_depth=4, min_samples_leaf=2, n_estimators=4.........
[CV 3/5; 134/400] END max_depth=4, min_samples_leaf=2, n_estimators=4;, score=0.794 total time= 0.0s
[CV 4/5; 134/400] START max_depth=4, min_samples_leaf=2, n_estimators=4.........
[CV 4/5; 134/400] END max_depth=4, min_samples_leaf=2, n_estimators=4;, score=0.793 total time= 0.0s
[CV 5/5; 134/400] START max_depth=4, min_samples_leaf=2, n_estimators=4.........
[CV 5/5; 134/400] END max_depth=4, min_samples_leaf=2, n_estimators=4;, score=0.783 total time= 0.0s
[CV 1/5; 135/400] START max_depth=4, min_samples_leaf=2, n_estimators=5.........
[CV 1/5; 135/400] END max_depth=4, min_samples_leaf=2, n_estimators=5;, score=0.777 total time= 0.0s
[CV 2/5; 135/400] START max_depth=4, min_samples_leaf=2, n_estimators=5.........
[CV 2/5; 135/400] END max_depth=4, min_samples_leaf=2, n_estimators=5;, score=0.798 total time= 0.0s
[CV 3/5; 135/400] START max_depth=4, min_samples_leaf=2, n_estimators=5.........
[CV 3/5; 135/400] END max_depth=4, min_samples_leaf=2, n_estimators=5;, score=0.794 total time= 0.0s
[CV 4/5; 135/400] START max_depth=4, min_samples_leaf=2, n_estimators=5.........
[CV 4/5; 135/400] END max_depth=4, min_samples_leaf=2, n_estimators=5;, score=0.791 total time= 0.0s
[CV 5/5; 135/400] START max_depth=4, min_samples_leaf=2, n_estimators=5.........
[CV 5/5; 135/400] END max_depth=4, min_samples_leaf=2, n_estimators=5;, score=0.777 total time= 0.0s
[CV 1/5; 136/400] START max_depth=4, min_samples_leaf=2, n_estimators=6.........
[CV 1/5; 136/400] END max_depth=4, min_samples_leaf=2, n_estimators=6;, score=0.797 total time= 0.0s
[CV 2/5; 136/400] START max_depth=4, min_samples_leaf=2, n_estimators=6.........
[CV 2/5; 136/400] END max_depth=4, min_samples_leaf=2, n_estimators=6;, score=0.790 total time= 0.0s
[CV 3/5; 136/400] START max_depth=4, min_samples_leaf=2, n_estimators=6.........
[CV 3/5; 136/400] END max_depth=4, min_samples_leaf=2, n_estimators=6;, score=0.784 total time= 0.0s
[CV 4/5; 136/400] START max_depth=4, min_samples_leaf=2, n_estimators=6.........
[CV 4/5; 136/400] END max_depth=4, min_samples_leaf=2, n_estimators=6;, score=0.796 total time= 0.0s
[CV 5/5; 136/400] START max_depth=4, min_samples_leaf=2, n_estimators=6.........
[CV 5/5; 136/400] END max_depth=4, min_samples_leaf=2, n_estimators=6;, score=0.780 total time= 0.0s
[CV 1/5; 137/400] START max_depth=4, min_samples_leaf=2, n_estimators=7.........
[CV 1/5; 137/400] END max_depth=4, min_samples_leaf=2, n_estimators=7;, score=0.799 total time= 0.0s
[CV 2/5; 137/400] START max_depth=4, min_samples_leaf=2, n_estimators=7.........
[CV 2/5; 137/400] END max_depth=4, min_samples_leaf=2, n_estimators=7;, score=0.788 total time= 0.0s
[CV 3/5; 137/400] START max_depth=4, min_samples_leaf=2, n_estimators=7.........
[CV 3/5; 137/400] END max_depth=4, min_samples_leaf=2, n_estimators=7;, score=0.783 total time= 0.0s
[CV 4/5; 137/400] START max_depth=4, min_samples_leaf=2, n_estimators=7.........
[CV 4/5; 137/400] END max_depth=4, min_samples_leaf=2, n_estimators=7;, score=0.795 total time= 0.0s
[CV 5/5; 137/400] START max_depth=4, min_samples_leaf=2, n_estimators=7.........
[CV 5/5; 137/400] END max_depth=4, min_samples_leaf=2, n_estimators=7;, score=0.786 total time= 0.0s
[CV 1/5; 138/400] START max_depth=4, min_samples_leaf=2, n_estimators=8.........
[CV 1/5; 138/400] END max_depth=4, min_samples_leaf=2, n_estimators=8;, score=0.801 total time= 0.0s
[CV 2/5; 138/400] START max_depth=4, min_samples_leaf=2, n_estimators=8.........
[CV 2/5; 138/400] END max_depth=4, min_samples_leaf=2, n_estimators=8;, score=0.792 total time= 0.0s
[CV 3/5; 138/400] START max_depth=4, min_samples_leaf=2, n_estimators=8.........
[CV 3/5; 138/400] END max_depth=4, min_samples_leaf=2, n_estimators=8;, score=0.787 total time= 0.0s
[CV 4/5; 138/400] START max_depth=4, min_samples_leaf=2, n_estimators=8.........
[CV 4/5; 138/400] END max_depth=4, min_samples_leaf=2, n_estimators=8;, score=0.793 total time= 0.0s
[CV 5/5; 138/400] START max_depth=4, min_samples_leaf=2, n_estimators=8.........
[CV 5/5; 138/400] END max_depth=4, min_samples_leaf=2, n_estimators=8;, score=0.774 total time= 0.0s
[CV 1/5; 139/400] START max_depth=4, min_samples_leaf=2, n_estimators=9.........
[CV 1/5; 139/400] END max_depth=4, min_samples_leaf=2, n_estimators=9;, score=0.800 total time= 0.0s
[CV 2/5; 139/400] START max_depth=4, min_samples_leaf=2, n_estimators=9.........
[CV 2/5; 139/400] END max_depth=4, min_samples_leaf=2, n_estimators=9;, score=0.801 total time= 0.0s
[CV 3/5; 139/400] START max_depth=4, min_samples_leaf=2, n_estimators=9.........
[CV 3/5; 139/400] END max_depth=4, min_samples_leaf=2, n_estimators=9;, score=0.786 total time= 0.0s
[CV 4/5; 139/400] START max_depth=4, min_samples_leaf=2, n_estimators=9.........
[CV 4/5; 139/400] END max_depth=4, min_samples_leaf=2, n_estimators=9;, score=0.794 total time= 0.0s
[CV 5/5; 139/400] START max_depth=4, min_samples_leaf=2, n_estimators=9.........
[CV 5/5; 139/400] END max_depth=4, min_samples_leaf=2, n_estimators=9;, score=0.783 total time= 0.0s
[CV 1/5; 140/400] START max_depth=4, min_samples_leaf=2, n_estimators=10........
[CV 1/5; 140/400] END max_depth=4, min_samples_leaf=2, n_estimators=10;, score=0.800 total time= 0.1s
[CV 2/5; 140/400] START max_depth=4, min_samples_leaf=2, n_estimators=10........
[CV 2/5; 140/400] END max_depth=4, min_samples_leaf=2, n_estimators=10;, score=0.790 total time= 0.1s
[CV 3/5; 140/400] START max_depth=4, min_samples_leaf=2, n_estimators=10........
[CV 3/5; 140/400] END max_depth=4, min_samples_leaf=2, n_estimators=10;, score=0.780 total time= 0.0s
[CV 4/5; 140/400] START max_depth=4, min_samples_leaf=2, n_estimators=10........
[CV 4/5; 140/400] END max_depth=4, min_samples_leaf=2, n_estimators=10;, score=0.791 total time= 0.1s
[CV 5/5; 140/400] START max_depth=4, min_samples_leaf=2, n_estimators=10........
[CV 5/5; 140/400] END max_depth=4, min_samples_leaf=2, n_estimators=10;, score=0.783 total time= 0.1s
[CV 1/5; 141/400] START max_depth=4, min_samples_leaf=3, n_estimators=1.........
[CV 1/5; 141/400] END max_depth=4, min_samples_leaf=3, n_estimators=1;, score=0.763 total time= 0.0s
[CV 2/5; 141/400] START max_depth=4, min_samples_leaf=3, n_estimators=1.........
[CV 2/5; 141/400] END max_depth=4, min_samples_leaf=3, n_estimators=1;, score=0.738 total time= 0.0s
[CV 3/5; 141/400] START max_depth=4, min_samples_leaf=3, n_estimators=1.........
[CV 3/5; 141/400] END max_depth=4, min_samples_leaf=3, n_estimators=1;, score=0.641 total time= 0.0s
[CV 4/5; 141/400] START max_depth=4, min_samples_leaf=3, n_estimators=1.........
[CV 4/5; 141/400] END max_depth=4, min_samples_leaf=3, n_estimators=1;, score=0.791 total time= 0.0s
[CV 5/5; 141/400] START max_depth=4, min_samples_leaf=3, n_estimators=1.........
[CV 5/5; 141/400] END max_depth=4, min_samples_leaf=3, n_estimators=1;, score=0.769 total time= 0.0s
[CV 1/5; 142/400] START max_depth=4, min_samples_leaf=3, n_estimators=2.........
[CV 1/5; 142/400] END max_depth=4, min_samples_leaf=3, n_estimators=2;, score=0.796 total time= 0.0s
[CV 2/5; 142/400] START max_depth=4, min_samples_leaf=3, n_estimators=2.........
[CV 2/5; 142/400] END max_depth=4, min_samples_leaf=3, n_estimators=2;, score=0.778 total time= 0.0s
[CV 3/5; 142/400] START max_depth=4, min_samples_leaf=3, n_estimators=2.........
[CV 3/5; 142/400] END max_depth=4, min_samples_leaf=3, n_estimators=2;, score=0.796 total time= 0.0s
[CV 4/5; 142/400] START max_depth=4, min_samples_leaf=3, n_estimators=2.........
[CV 4/5; 142/400] END max_depth=4, min_samples_leaf=3, n_estimators=2;, score=0.789 total time= 0.0s
[CV 5/5; 142/400] START max_depth=4, min_samples_leaf=3, n_estimators=2.........
[CV 5/5; 142/400] END max_depth=4, min_samples_leaf=3, n_estimators=2;, score=0.780 total time= 0.0s
[CV 1/5; 143/400] START max_depth=4, min_samples_leaf=3, n_estimators=3.........
[CV 1/5; 143/400] END max_depth=4, min_samples_leaf=3, n_estimators=3;, score=0.804 total time= 0.0s
[CV 2/5; 143/400] START max_depth=4, min_samples_leaf=3, n_estimators=3.........
[CV 2/5; 143/400] END max_depth=4, min_samples_leaf=3, n_estimators=3;, score=0.802 total time= 0.0s
[CV 3/5; 143/400] START max_depth=4, min_samples_leaf=3, n_estimators=3.........
[CV 3/5; 143/400] END max_depth=4, min_samples_leaf=3, n_estimators=3;, score=0.780 total time= 0.0s
[CV 4/5; 143/400] START max_depth=4, min_samples_leaf=3, n_estimators=3.........
[CV 4/5; 143/400] END max_depth=4, min_samples_leaf=3, n_estimators=3;, score=0.785 total time= 0.0s
[CV 5/5; 143/400] START max_depth=4, min_samples_leaf=3, n_estimators=3.........
[CV 5/5; 143/400] END max_depth=4, min_samples_leaf=3, n_estimators=3;, score=0.777 total time= 0.0s
[CV 1/5; 144/400] START max_depth=4, min_samples_leaf=3, n_estimators=4.........
[CV 1/5; 144/400] END max_depth=4, min_samples_leaf=3, n_estimators=4;, score=0.797 total time= 0.0s
[CV 2/5; 144/400] START max_depth=4, min_samples_leaf=3, n_estimators=4.........
[CV 2/5; 144/400] END max_depth=4, min_samples_leaf=3, n_estimators=4;, score=0.789 total time= 0.0s
[CV 3/5; 144/400] START max_depth=4, min_samples_leaf=3, n_estimators=4.........
[CV 3/5; 144/400] END max_depth=4, min_samples_leaf=3, n_estimators=4;, score=0.782 total time= 0.0s
[CV 4/5; 144/400] START max_depth=4, min_samples_leaf=3, n_estimators=4.........
[CV 4/5; 144/400] END max_depth=4, min_samples_leaf=3, n_estimators=4;, score=0.796 total time= 0.0s
[CV 5/5; 144/400] START max_depth=4, min_samples_leaf=3, n_estimators=4.........
[CV 5/5; 144/400] END max_depth=4, min_samples_leaf=3, n_estimators=4;, score=0.778 total time= 0.0s
[CV 1/5; 145/400] START max_depth=4, min_samples_leaf=3, n_estimators=5.........
[CV 1/5; 145/400] END max_depth=4, min_samples_leaf=3, n_estimators=5;, score=0.800 total time= 0.0s
[CV 2/5; 145/400] START max_depth=4, min_samples_leaf=3, n_estimators=5.........
[CV 2/5; 145/400] END max_depth=4, min_samples_leaf=3, n_estimators=5;, score=0.801 total time= 0.0s
[CV 3/5; 145/400] START max_depth=4, min_samples_leaf=3, n_estimators=5.........
[CV 3/5; 145/400] END max_depth=4, min_samples_leaf=3, n_estimators=5;, score=0.799 total time= 0.0s
[CV 4/5; 145/400] START max_depth=4, min_samples_leaf=3, n_estimators=5.........
[CV 4/5; 145/400] END max_depth=4, min_samples_leaf=3, n_estimators=5;, score=0.796 total time= 0.0s
[CV 5/5; 145/400] START max_depth=4, min_samples_leaf=3, n_estimators=5.........
[CV 5/5; 145/400] END max_depth=4, min_samples_leaf=3, n_estimators=5;, score=0.791 total time= 0.0s
[CV 1/5; 146/400] START max_depth=4, min_samples_leaf=3, n_estimators=6.........
[CV 1/5; 146/400] END max_depth=4, min_samples_leaf=3, n_estimators=6;, score=0.801 total time= 0.0s
[CV 2/5; 146/400] START max_depth=4, min_samples_leaf=3, n_estimators=6.........
[CV 2/5; 146/400] END max_depth=4, min_samples_leaf=3, n_estimators=6;, score=0.794 total time= 0.0s
[CV 3/5; 146/400] START max_depth=4, min_samples_leaf=3, n_estimators=6.........
[CV 3/5; 146/400] END max_depth=4, min_samples_leaf=3, n_estimators=6;, score=0.779 total time= 0.0s
[CV 4/5; 146/400] START max_depth=4, min_samples_leaf=3, n_estimators=6.........
[CV 4/5; 146/400] END max_depth=4, min_samples_leaf=3, n_estimators=6;, score=0.794 total time= 0.0s
[CV 5/5; 146/400] START max_depth=4, min_samples_leaf=3, n_estimators=6.........
[CV 5/5; 146/400] END max_depth=4, min_samples_leaf=3, n_estimators=6;, score=0.777 total time= 0.0s
[CV 1/5; 147/400] START max_depth=4, min_samples_leaf=3, n_estimators=7.........
[CV 1/5; 147/400] END max_depth=4, min_samples_leaf=3, n_estimators=7;, score=0.801 total time= 0.0s
[CV 2/5; 147/400] START max_depth=4, min_samples_leaf=3, n_estimators=7.........
[CV 2/5; 147/400] END max_depth=4, min_samples_leaf=3, n_estimators=7;, score=0.792 total time= 0.0s
[CV 3/5; 147/400] START max_depth=4, min_samples_leaf=3, n_estimators=7.........
[CV 3/5; 147/400] END max_depth=4, min_samples_leaf=3, n_estimators=7;, score=0.789 total time= 0.0s
[CV 4/5; 147/400] START max_depth=4, min_samples_leaf=3, n_estimators=7.........
[CV 4/5; 147/400] END max_depth=4, min_samples_leaf=3, n_estimators=7;, score=0.791 total time= 0.0s
[CV 5/5; 147/400] START max_depth=4, min_samples_leaf=3, n_estimators=7.........
[CV 5/5; 147/400] END max_depth=4, min_samples_leaf=3, n_estimators=7;, score=0.773 total time= 0.0s
[CV 1/5; 148/400] START max_depth=4, min_samples_leaf=3, n_estimators=8.........
[CV 1/5; 148/400] END max_depth=4, min_samples_leaf=3, n_estimators=8;, score=0.804 total time= 0.0s
[CV 2/5; 148/400] START max_depth=4, min_samples_leaf=3, n_estimators=8.........
[CV 2/5; 148/400] END max_depth=4, min_samples_leaf=3, n_estimators=8;, score=0.790 total time= 0.0s
[CV 3/5; 148/400] START max_depth=4, min_samples_leaf=3, n_estimators=8.........
[CV 3/5; 148/400] END max_depth=4, min_samples_leaf=3, n_estimators=8;, score=0.782 total time= 0.0s
[CV 4/5; 148/400] START max_depth=4, min_samples_leaf=3, n_estimators=8.........
[CV 4/5; 148/400] END max_depth=4, min_samples_leaf=3, n_estimators=8;, score=0.796 total time= 0.0s
[CV 5/5; 148/400] START max_depth=4, min_samples_leaf=3, n_estimators=8.........
[CV 5/5; 148/400] END max_depth=4, min_samples_leaf=3, n_estimators=8;, score=0.777 total time= 0.1s
[CV 1/5; 149/400] START max_depth=4, min_samples_leaf=3, n_estimators=9.........
[CV 1/5; 149/400] END max_depth=4, min_samples_leaf=3, n_estimators=9;, score=0.796 total time= 0.0s
[CV 2/5; 149/400] START max_depth=4, min_samples_leaf=3, n_estimators=9.........
[CV 2/5; 149/400] END max_depth=4, min_samples_leaf=3, n_estimators=9;, score=0.798 total time= 0.1s
[CV 3/5; 149/400] START max_depth=4, min_samples_leaf=3, n_estimators=9.........
[CV 3/5; 149/400] END max_depth=4, min_samples_leaf=3, n_estimators=9;, score=0.792 total time= 0.0s
[CV 4/5; 149/400] START max_depth=4, min_samples_leaf=3, n_estimators=9.........
[CV 4/5; 149/400] END max_depth=4, min_samples_leaf=3, n_estimators=9;, score=0.796 total time= 0.0s
[CV 5/5; 149/400] START max_depth=4, min_samples_leaf=3, n_estimators=9.........
[CV 5/5; 149/400] END max_depth=4, min_samples_leaf=3, n_estimators=9;, score=0.774 total time= 0.1s
[CV 1/5; 150/400] START max_depth=4, min_samples_leaf=3, n_estimators=10........
[CV 1/5; 150/400] END max_depth=4, min_samples_leaf=3, n_estimators=10;, score=0.797 total time= 0.0s
[CV 2/5; 150/400] START max_depth=4, min_samples_leaf=3, n_estimators=10........
[CV 2/5; 150/400] END max_depth=4, min_samples_leaf=3, n_estimators=10;, score=0.792 total time= 0.0s
[CV 3/5; 150/400] START max_depth=4, min_samples_leaf=3, n_estimators=10........
[CV 3/5; 150/400] END max_depth=4, min_samples_leaf=3, n_estimators=10;, score=0.790 total time= 0.1s
[CV 4/5; 150/400] START max_depth=4, min_samples_leaf=3, n_estimators=10........
[CV 4/5; 150/400] END max_depth=4, min_samples_leaf=3, n_estimators=10;, score=0.796 total time= 0.1s
[CV 5/5; 150/400] START max_depth=4, min_samples_leaf=3, n_estimators=10........
[CV 5/5; 150/400] END max_depth=4, min_samples_leaf=3, n_estimators=10;, score=0.776 total time= 0.0s
[CV 1/5; 151/400] START max_depth=4, min_samples_leaf=4, n_estimators=1.........
[CV 1/5; 151/400] END max_depth=4, min_samples_leaf=4, n_estimators=1;, score=0.769 total time= 0.0s
[CV 2/5; 151/400] START max_depth=4, min_samples_leaf=4, n_estimators=1.........
[CV 2/5; 151/400] END max_depth=4, min_samples_leaf=4, n_estimators=1;, score=0.682 total time= 0.0s
[CV 3/5; 151/400] START max_depth=4, min_samples_leaf=4, n_estimators=1.........
[CV 3/5; 151/400] END max_depth=4, min_samples_leaf=4, n_estimators=1;, score=0.680 total time= 0.0s
[CV 4/5; 151/400] START max_depth=4, min_samples_leaf=4, n_estimators=1.........
[CV 4/5; 151/400] END max_depth=4, min_samples_leaf=4, n_estimators=1;, score=0.771 total time= 0.0s
[CV 5/5; 151/400] START max_depth=4, min_samples_leaf=4, n_estimators=1.........
[CV 5/5; 151/400] END max_depth=4, min_samples_leaf=4, n_estimators=1;, score=0.717 total time= 0.0s
[CV 1/5; 152/400] START max_depth=4, min_samples_leaf=4, n_estimators=2.........
[CV 1/5; 152/400] END max_depth=4, min_samples_leaf=4, n_estimators=2;, score=0.797 total time= 0.0s
[CV 2/5; 152/400] START max_depth=4, min_samples_leaf=4, n_estimators=2.........
[CV 2/5; 152/400] END max_depth=4, min_samples_leaf=4, n_estimators=2;, score=0.802 total time= 0.0s
[CV 3/5; 152/400] START max_depth=4, min_samples_leaf=4, n_estimators=2.........
[CV 3/5; 152/400] END max_depth=4, min_samples_leaf=4, n_estimators=2;, score=0.779 total time= 0.0s
[CV 4/5; 152/400] START max_depth=4, min_samples_leaf=4, n_estimators=2.........
[CV 4/5; 152/400] END max_depth=4, min_samples_leaf=4, n_estimators=2;, score=0.798 total time= 0.0s
[CV 5/5; 152/400] START max_depth=4, min_samples_leaf=4, n_estimators=2.........
[CV 5/5; 152/400] END max_depth=4, min_samples_leaf=4, n_estimators=2;, score=0.766 total time= 0.0s
[CV 1/5; 153/400] START max_depth=4, min_samples_leaf=4, n_estimators=3.........
[CV 1/5; 153/400] END max_depth=4, min_samples_leaf=4, n_estimators=3;, score=0.809 total time= 0.0s
[CV 2/5; 153/400] START max_depth=4, min_samples_leaf=4, n_estimators=3.........
[CV 2/5; 153/400] END max_depth=4, min_samples_leaf=4, n_estimators=3;, score=0.801 total time= 0.0s
[CV 3/5; 153/400] START max_depth=4, min_samples_leaf=4, n_estimators=3.........
[CV 3/5; 153/400] END max_depth=4, min_samples_leaf=4, n_estimators=3;, score=0.774 total time= 0.0s
[CV 4/5; 153/400] START max_depth=4, min_samples_leaf=4, n_estimators=3.........
[CV 4/5; 153/400] END max_depth=4, min_samples_leaf=4, n_estimators=3;, score=0.793 total time= 0.0s
[CV 5/5; 153/400] START max_depth=4, min_samples_leaf=4, n_estimators=3.........
[CV 5/5; 153/400] END max_depth=4, min_samples_leaf=4, n_estimators=3;, score=0.774 total time= 0.0s
[CV 1/5; 154/400] START max_depth=4, min_samples_leaf=4, n_estimators=4.........
[CV 1/5; 154/400] END max_depth=4, min_samples_leaf=4, n_estimators=4;, score=0.795 total time= 0.0s
[CV 2/5; 154/400] START max_depth=4, min_samples_leaf=4, n_estimators=4.........
[CV 2/5; 154/400] END max_depth=4, min_samples_leaf=4, n_estimators=4;, score=0.796 total time= 0.0s
[CV 3/5; 154/400] START max_depth=4, min_samples_leaf=4, n_estimators=4.........
[CV 3/5; 154/400] END max_depth=4, min_samples_leaf=4, n_estimators=4;, score=0.783 total time= 0.0s
[CV 4/5; 154/400] START max_depth=4, min_samples_leaf=4, n_estimators=4.........
[CV 4/5; 154/400] END max_depth=4, min_samples_leaf=4, n_estimators=4;, score=0.807 total time= 0.0s
[CV 5/5; 154/400] START max_depth=4, min_samples_leaf=4, n_estimators=4.........
[CV 5/5; 154/400] END max_depth=4, min_samples_leaf=4, n_estimators=4;, score=0.785 total time= 0.0s
[CV 1/5; 155/400] START max_depth=4, min_samples_leaf=4, n_estimators=5.........
[CV 1/5; 155/400] END max_depth=4, min_samples_leaf=4, n_estimators=5;, score=0.798 total time= 0.0s
[CV 2/5; 155/400] START max_depth=4, min_samples_leaf=4, n_estimators=5.........
[CV 2/5; 155/400] END max_depth=4, min_samples_leaf=4, n_estimators=5;, score=0.794 total time= 0.0s
[CV 3/5; 155/400] START max_depth=4, min_samples_leaf=4, n_estimators=5.........
[CV 3/5; 155/400] END max_depth=4, min_samples_leaf=4, n_estimators=5;, score=0.788 total time= 0.0s
[CV 4/5; 155/400] START max_depth=4, min_samples_leaf=4, n_estimators=5.........
[CV 4/5; 155/400] END max_depth=4, min_samples_leaf=4, n_estimators=5;, score=0.798 total time= 0.0s
[CV 5/5; 155/400] START max_depth=4, min_samples_leaf=4, n_estimators=5.........
[CV 5/5; 155/400] END max_depth=4, min_samples_leaf=4, n_estimators=5;, score=0.781 total time= 0.0s
[CV 1/5; 156/400] START max_depth=4, min_samples_leaf=4, n_estimators=6.........
[CV 1/5; 156/400] END max_depth=4, min_samples_leaf=4, n_estimators=6;, score=0.796 total time= 0.0s
[CV 2/5; 156/400] START max_depth=4, min_samples_leaf=4, n_estimators=6.........
[CV 2/5; 156/400] END max_depth=4, min_samples_leaf=4, n_estimators=6;, score=0.796 total time= 0.1s
[CV 3/5; 156/400] START max_depth=4, min_samples_leaf=4, n_estimators=6.........
[CV 3/5; 156/400] END max_depth=4, min_samples_leaf=4, n_estimators=6;, score=0.794 total time= 0.0s
[CV 4/5; 156/400] START max_depth=4, min_samples_leaf=4, n_estimators=6.........
[CV 4/5; 156/400] END max_depth=4, min_samples_leaf=4, n_estimators=6;, score=0.791 total time= 0.0s
[CV 5/5; 156/400] START max_depth=4, min_samples_leaf=4, n_estimators=6.........
[CV 5/5; 156/400] END max_depth=4, min_samples_leaf=4, n_estimators=6;, score=0.783 total time= 0.0s
[CV 1/5; 157/400] START max_depth=4, min_samples_leaf=4, n_estimators=7.........
[CV 1/5; 157/400] END max_depth=4, min_samples_leaf=4, n_estimators=7;, score=0.797 total time= 0.0s
[CV 2/5; 157/400] START max_depth=4, min_samples_leaf=4, n_estimators=7.........
[CV 2/5; 157/400] END max_depth=4, min_samples_leaf=4, n_estimators=7;, score=0.794 total time= 0.0s
[CV 3/5; 157/400] START max_depth=4, min_samples_leaf=4, n_estimators=7.........
[CV 3/5; 157/400] END max_depth=4, min_samples_leaf=4, n_estimators=7;, score=0.785 total time= 0.1s
[CV 4/5; 157/400] START max_depth=4, min_samples_leaf=4, n_estimators=7.........
[CV 4/5; 157/400] END max_depth=4, min_samples_leaf=4, n_estimators=7;, score=0.803 total time= 0.0s
[CV 5/5; 157/400] START max_depth=4, min_samples_leaf=4, n_estimators=7.........
[CV 5/5; 157/400] END max_depth=4, min_samples_leaf=4, n_estimators=7;, score=0.782 total time= 0.0s
[CV 1/5; 158/400] START max_depth=4, min_samples_leaf=4, n_estimators=8.........
[CV 1/5; 158/400] END max_depth=4, min_samples_leaf=4, n_estimators=8;, score=0.798 total time= 0.0s
[CV 2/5; 158/400] START max_depth=4, min_samples_leaf=4, n_estimators=8.........
[CV 2/5; 158/400] END max_depth=4, min_samples_leaf=4, n_estimators=8;, score=0.791 total time= 0.0s
[CV 3/5; 158/400] START max_depth=4, min_samples_leaf=4, n_estimators=8.........
[CV 3/5; 158/400] END max_depth=4, min_samples_leaf=4, n_estimators=8;, score=0.781 total time= 0.0s
[CV 4/5; 158/400] START max_depth=4, min_samples_leaf=4, n_estimators=8.........
[CV 4/5; 158/400] END max_depth=4, min_samples_leaf=4, n_estimators=8;, score=0.796 total time= 0.0s
[CV 5/5; 158/400] START max_depth=4, min_samples_leaf=4, n_estimators=8.........
[CV 5/5; 158/400] END max_depth=4, min_samples_leaf=4, n_estimators=8;, score=0.780 total time= 0.0s
[CV 1/5; 159/400] START max_depth=4, min_samples_leaf=4, n_estimators=9.........
[CV 1/5; 159/400] END max_depth=4, min_samples_leaf=4, n_estimators=9;, score=0.798 total time= 0.0s
[CV 2/5; 159/400] START max_depth=4, min_samples_leaf=4, n_estimators=9.........
[CV 2/5; 159/400] END max_depth=4, min_samples_leaf=4, n_estimators=9;, score=0.795 total time= 0.0s
[CV 3/5; 159/400] START max_depth=4, min_samples_leaf=4, n_estimators=9.........
[CV 3/5; 159/400] END max_depth=4, min_samples_leaf=4, n_estimators=9;, score=0.783 total time= 0.0s
[CV 4/5; 159/400] START max_depth=4, min_samples_leaf=4, n_estimators=9.........
[CV 4/5; 159/400] END max_depth=4, min_samples_leaf=4, n_estimators=9;, score=0.792 total time= 0.0s
[CV 5/5; 159/400] START max_depth=4, min_samples_leaf=4, n_estimators=9.........
[CV 5/5; 159/400] END max_depth=4, min_samples_leaf=4, n_estimators=9;, score=0.776 total time= 0.0s
[CV 1/5; 160/400] START max_depth=4, min_samples_leaf=4, n_estimators=10........
[CV 1/5; 160/400] END max_depth=4, min_samples_leaf=4, n_estimators=10;, score=0.796 total time= 0.0s
[CV 2/5; 160/400] START max_depth=4, min_samples_leaf=4, n_estimators=10........
[CV 2/5; 160/400] END max_depth=4, min_samples_leaf=4, n_estimators=10;, score=0.792 total time= 0.1s
[CV 3/5; 160/400] START max_depth=4, min_samples_leaf=4, n_estimators=10........
[CV 3/5; 160/400] END max_depth=4, min_samples_leaf=4, n_estimators=10;, score=0.794 total time= 0.1s
[CV 4/5; 160/400] START max_depth=4, min_samples_leaf=4, n_estimators=10........
[CV 4/5; 160/400] END max_depth=4, min_samples_leaf=4, n_estimators=10;, score=0.794 total time= 0.0s
[CV 5/5; 160/400] START max_depth=4, min_samples_leaf=4, n_estimators=10........
[CV 5/5; 160/400] END max_depth=4, min_samples_leaf=4, n_estimators=10;, score=0.781 total time= 0.1s
[CV 1/5; 161/400] START max_depth=5, min_samples_leaf=1, n_estimators=1.........
[CV 1/5; 161/400] END max_depth=5, min_samples_leaf=1, n_estimators=1;, score=0.643 total time= 0.0s
[CV 2/5; 161/400] START max_depth=5, min_samples_leaf=1, n_estimators=1.........
[CV 2/5; 161/400] END max_depth=5, min_samples_leaf=1, n_estimators=1;, score=0.788 total time= 0.0s
[CV 3/5; 161/400] START max_depth=5, min_samples_leaf=1, n_estimators=1.........
[CV 3/5; 161/400] END max_depth=5, min_samples_leaf=1, n_estimators=1;, score=0.807 total time= 0.0s
[CV 4/5; 161/400] START max_depth=5, min_samples_leaf=1, n_estimators=1.........
[CV 4/5; 161/400] END max_depth=5, min_samples_leaf=1, n_estimators=1;, score=0.793 total time= 0.0s
[CV 5/5; 161/400] START max_depth=5, min_samples_leaf=1, n_estimators=1.........
[CV 5/5; 161/400] END max_depth=5, min_samples_leaf=1, n_estimators=1;, score=0.763 total time= 0.0s
[CV 1/5; 162/400] START max_depth=5, min_samples_leaf=1, n_estimators=2.........
[CV 1/5; 162/400] END max_depth=5, min_samples_leaf=1, n_estimators=2;, score=0.744 total time= 0.0s
[CV 2/5; 162/400] START max_depth=5, min_samples_leaf=1, n_estimators=2.........
[CV 2/5; 162/400] END max_depth=5, min_samples_leaf=1, n_estimators=2;, score=0.794 total time= 0.0s
[CV 3/5; 162/400] START max_depth=5, min_samples_leaf=1, n_estimators=2.........
[CV 3/5; 162/400] END max_depth=5, min_samples_leaf=1, n_estimators=2;, score=0.783 total time= 0.0s
[CV 4/5; 162/400] START max_depth=5, min_samples_leaf=1, n_estimators=2.........
[CV 4/5; 162/400] END max_depth=5, min_samples_leaf=1, n_estimators=2;, score=0.773 total time= 0.0s
[CV 5/5; 162/400] START max_depth=5, min_samples_leaf=1, n_estimators=2.........
[CV 5/5; 162/400] END max_depth=5, min_samples_leaf=1, n_estimators=2;, score=0.790 total time= 0.0s
[CV 1/5; 163/400] START max_depth=5, min_samples_leaf=1, n_estimators=3.........
[CV 1/5; 163/400] END max_depth=5, min_samples_leaf=1, n_estimators=3;, score=0.802 total time= 0.0s
[CV 2/5; 163/400] START max_depth=5, min_samples_leaf=1, n_estimators=3.........
[CV 2/5; 163/400] END max_depth=5, min_samples_leaf=1, n_estimators=3;, score=0.802 total time= 0.0s
[CV 3/5; 163/400] START max_depth=5, min_samples_leaf=1, n_estimators=3.........
[CV 3/5; 163/400] END max_depth=5, min_samples_leaf=1, n_estimators=3;, score=0.788 total time= 0.0s
[CV 4/5; 163/400] START max_depth=5, min_samples_leaf=1, n_estimators=3.........
[CV 4/5; 163/400] END max_depth=5, min_samples_leaf=1, n_estimators=3;, score=0.801 total time= 0.0s
[CV 5/5; 163/400] START max_depth=5, min_samples_leaf=1, n_estimators=3.........
[CV 5/5; 163/400] END max_depth=5, min_samples_leaf=1, n_estimators=3;, score=0.784 total time= 0.0s
[CV 1/5; 164/400] START max_depth=5, min_samples_leaf=1, n_estimators=4.........
[CV 1/5; 164/400] END max_depth=5, min_samples_leaf=1, n_estimators=4;, score=0.785 total time= 0.0s
[CV 2/5; 164/400] START max_depth=5, min_samples_leaf=1, n_estimators=4.........
[CV 2/5; 164/400] END max_depth=5, min_samples_leaf=1, n_estimators=4;, score=0.807 total time= 0.0s
[CV 3/5; 164/400] START max_depth=5, min_samples_leaf=1, n_estimators=4.........
[CV 3/5; 164/400] END max_depth=5, min_samples_leaf=1, n_estimators=4;, score=0.801 total time= 0.0s
[CV 4/5; 164/400] START max_depth=5, min_samples_leaf=1, n_estimators=4.........
[CV 4/5; 164/400] END max_depth=5, min_samples_leaf=1, n_estimators=4;, score=0.808 total time= 0.0s
[CV 5/5; 164/400] START max_depth=5, min_samples_leaf=1, n_estimators=4.........
[CV 5/5; 164/400] END max_depth=5, min_samples_leaf=1, n_estimators=4;, score=0.774 total time= 0.0s
[CV 1/5; 165/400] START max_depth=5, min_samples_leaf=1, n_estimators=5.........
[CV 1/5; 165/400] END max_depth=5, min_samples_leaf=1, n_estimators=5;, score=0.814 total time= 0.0s
[CV 2/5; 165/400] START max_depth=5, min_samples_leaf=1, n_estimators=5.........
[CV 2/5; 165/400] END max_depth=5, min_samples_leaf=1, n_estimators=5;, score=0.809 total time= 0.0s
[CV 3/5; 165/400] START max_depth=5, min_samples_leaf=1, n_estimators=5.........
[CV 3/5; 165/400] END max_depth=5, min_samples_leaf=1, n_estimators=5;, score=0.792 total time= 0.0s
[CV 4/5; 165/400] START max_depth=5, min_samples_leaf=1, n_estimators=5.........
[CV 4/5; 165/400] END max_depth=5, min_samples_leaf=1, n_estimators=5;, score=0.807 total time= 0.0s
[CV 5/5; 165/400] START max_depth=5, min_samples_leaf=1, n_estimators=5.........
[CV 5/5; 165/400] END max_depth=5, min_samples_leaf=1, n_estimators=5;, score=0.808 total time= 0.0s
[CV 1/5; 166/400] START max_depth=5, min_samples_leaf=1, n_estimators=6.........
[CV 1/5; 166/400] END max_depth=5, min_samples_leaf=1, n_estimators=6;, score=0.805 total time= 0.0s
[CV 2/5; 166/400] START max_depth=5, min_samples_leaf=1, n_estimators=6.........
[CV 2/5; 166/400] END max_depth=5, min_samples_leaf=1, n_estimators=6;, score=0.803 total time= 0.0s
[CV 3/5; 166/400] START max_depth=5, min_samples_leaf=1, n_estimators=6.........
[CV 3/5; 166/400] END max_depth=5, min_samples_leaf=1, n_estimators=6;, score=0.801 total time= 0.0s
[CV 4/5; 166/400] START max_depth=5, min_samples_leaf=1, n_estimators=6.........
[CV 4/5; 166/400] END max_depth=5, min_samples_leaf=1, n_estimators=6;, score=0.801 total time= 0.0s
[CV 5/5; 166/400] START max_depth=5, min_samples_leaf=1, n_estimators=6.........
[CV 5/5; 166/400] END max_depth=5, min_samples_leaf=1, n_estimators=6;, score=0.794 total time= 0.0s
[CV 1/5; 167/400] START max_depth=5, min_samples_leaf=1, n_estimators=7.........
[CV 1/5; 167/400] END max_depth=5, min_samples_leaf=1, n_estimators=7;, score=0.810 total time= 0.0s
[CV 2/5; 167/400] START max_depth=5, min_samples_leaf=1, n_estimators=7.........
[CV 2/5; 167/400] END max_depth=5, min_samples_leaf=1, n_estimators=7;, score=0.808 total time= 0.0s
[CV 3/5; 167/400] START max_depth=5, min_samples_leaf=1, n_estimators=7.........
[CV 3/5; 167/400] END max_depth=5, min_samples_leaf=1, n_estimators=7;, score=0.792 total time= 0.0s
[CV 4/5; 167/400] START max_depth=5, min_samples_leaf=1, n_estimators=7.........
[CV 4/5; 167/400] END max_depth=5, min_samples_leaf=1, n_estimators=7;, score=0.805 total time= 0.0s
[CV 5/5; 167/400] START max_depth=5, min_samples_leaf=1, n_estimators=7.........
[CV 5/5; 167/400] END max_depth=5, min_samples_leaf=1, n_estimators=7;, score=0.789 total time= 0.0s
[CV 1/5; 168/400] START max_depth=5, min_samples_leaf=1, n_estimators=8.........
[CV 1/5; 168/400] END max_depth=5, min_samples_leaf=1, n_estimators=8;, score=0.813 total time= 0.0s
[CV 2/5; 168/400] START max_depth=5, min_samples_leaf=1, n_estimators=8.........
[CV 2/5; 168/400] END max_depth=5, min_samples_leaf=1, n_estimators=8;, score=0.801 total time= 0.1s
[CV 3/5; 168/400] START max_depth=5, min_samples_leaf=1, n_estimators=8.........
[CV 3/5; 168/400] END max_depth=5, min_samples_leaf=1, n_estimators=8;, score=0.807 total time= 0.0s
[CV 4/5; 168/400] START max_depth=5, min_samples_leaf=1, n_estimators=8.........
[CV 4/5; 168/400] END max_depth=5, min_samples_leaf=1, n_estimators=8;, score=0.796 total time= 0.0s
[CV 5/5; 168/400] START max_depth=5, min_samples_leaf=1, n_estimators=8.........
[CV 5/5; 168/400] END max_depth=5, min_samples_leaf=1, n_estimators=8;, score=0.794 total time= 0.0s
[CV 1/5; 169/400] START max_depth=5, min_samples_leaf=1, n_estimators=9.........
[CV 1/5; 169/400] END max_depth=5, min_samples_leaf=1, n_estimators=9;, score=0.808 total time= 0.1s
[CV 2/5; 169/400] START max_depth=5, min_samples_leaf=1, n_estimators=9.........
[CV 2/5; 169/400] END max_depth=5, min_samples_leaf=1, n_estimators=9;, score=0.799 total time= 0.0s
[CV 3/5; 169/400] START max_depth=5, min_samples_leaf=1, n_estimators=9.........
[CV 3/5; 169/400] END max_depth=5, min_samples_leaf=1, n_estimators=9;, score=0.797 total time= 0.0s
[CV 4/5; 169/400] START max_depth=5, min_samples_leaf=1, n_estimators=9.........
[CV 4/5; 169/400] END max_depth=5, min_samples_leaf=1, n_estimators=9;, score=0.808 total time= 0.0s
[CV 5/5; 169/400] START max_depth=5, min_samples_leaf=1, n_estimators=9.........
[CV 5/5; 169/400] END max_depth=5, min_samples_leaf=1, n_estimators=9;, score=0.780 total time= 0.0s
[CV 1/5; 170/400] START max_depth=5, min_samples_leaf=1, n_estimators=10........
[CV 1/5; 170/400] END max_depth=5, min_samples_leaf=1, n_estimators=10;, score=0.807 total time= 0.1s
[CV 2/5; 170/400] START max_depth=5, min_samples_leaf=1, n_estimators=10........
[CV 2/5; 170/400] END max_depth=5, min_samples_leaf=1, n_estimators=10;, score=0.808 total time= 0.1s
[CV 3/5; 170/400] START max_depth=5, min_samples_leaf=1, n_estimators=10........
[CV 3/5; 170/400] END max_depth=5, min_samples_leaf=1, n_estimators=10;, score=0.801 total time= 0.1s
[CV 4/5; 170/400] START max_depth=5, min_samples_leaf=1, n_estimators=10........
[CV 4/5; 170/400] END max_depth=5, min_samples_leaf=1, n_estimators=10;, score=0.799 total time= 0.1s
[CV 5/5; 170/400] START max_depth=5, min_samples_leaf=1, n_estimators=10........
[CV 5/5; 170/400] END max_depth=5, min_samples_leaf=1, n_estimators=10;, score=0.780 total time= 0.1s
[CV 1/5; 171/400] START max_depth=5, min_samples_leaf=2, n_estimators=1.........
[CV 1/5; 171/400] END max_depth=5, min_samples_leaf=2, n_estimators=1;, score=0.796 total time= 0.0s
[CV 2/5; 171/400] START max_depth=5, min_samples_leaf=2, n_estimators=1.........
[CV 2/5; 171/400] END max_depth=5, min_samples_leaf=2, n_estimators=1;, score=0.696 total time= 0.0s
[CV 3/5; 171/400] START max_depth=5, min_samples_leaf=2, n_estimators=1.........
[CV 3/5; 171/400] END max_depth=5, min_samples_leaf=2, n_estimators=1;, score=0.792 total time= 0.0s
[CV 4/5; 171/400] START max_depth=5, min_samples_leaf=2, n_estimators=1.........
[CV 4/5; 171/400] END max_depth=5, min_samples_leaf=2, n_estimators=1;, score=0.804 total time= 0.0s
[CV 5/5; 171/400] START max_depth=5, min_samples_leaf=2, n_estimators=1.........
[CV 5/5; 171/400] END max_depth=5, min_samples_leaf=2, n_estimators=1;, score=0.708 total time= 0.0s
[CV 1/5; 172/400] START max_depth=5, min_samples_leaf=2, n_estimators=2.........
[CV 1/5; 172/400] END max_depth=5, min_samples_leaf=2, n_estimators=2;, score=0.799 total time= 0.0s
[CV 2/5; 172/400] START max_depth=5, min_samples_leaf=2, n_estimators=2.........
[CV 2/5; 172/400] END max_depth=5, min_samples_leaf=2, n_estimators=2;, score=0.794 total time= 0.0s
[CV 3/5; 172/400] START max_depth=5, min_samples_leaf=2, n_estimators=2.........
[CV 3/5; 172/400] END max_depth=5, min_samples_leaf=2, n_estimators=2;, score=0.782 total time= 0.0s
[CV 4/5; 172/400] START max_depth=5, min_samples_leaf=2, n_estimators=2.........
[CV 4/5; 172/400] END max_depth=5, min_samples_leaf=2, n_estimators=2;, score=0.805 total time= 0.0s
[CV 5/5; 172/400] START max_depth=5, min_samples_leaf=2, n_estimators=2.........
[CV 5/5; 172/400] END max_depth=5, min_samples_leaf=2, n_estimators=2;, score=0.777 total time= 0.0s
[CV 1/5; 173/400] START max_depth=5, min_samples_leaf=2, n_estimators=3.........
[CV 1/5; 173/400] END max_depth=5, min_samples_leaf=2, n_estimators=3;, score=0.807 total time= 0.0s
[CV 2/5; 173/400] START max_depth=5, min_samples_leaf=2, n_estimators=3.........
[CV 2/5; 173/400] END max_depth=5, min_samples_leaf=2, n_estimators=3;, score=0.795 total time= 0.0s
[CV 3/5; 173/400] START max_depth=5, min_samples_leaf=2, n_estimators=3.........
[CV 3/5; 173/400] END max_depth=5, min_samples_leaf=2, n_estimators=3;, score=0.788 total time= 0.0s
[CV 4/5; 173/400] START max_depth=5, min_samples_leaf=2, n_estimators=3.........
[CV 4/5; 173/400] END max_depth=5, min_samples_leaf=2, n_estimators=3;, score=0.805 total time= 0.0s
[CV 5/5; 173/400] START max_depth=5, min_samples_leaf=2, n_estimators=3.........
[CV 5/5; 173/400] END max_depth=5, min_samples_leaf=2, n_estimators=3;, score=0.791 total time= 0.0s
[CV 1/5; 174/400] START max_depth=5, min_samples_leaf=2, n_estimators=4.........
[CV 1/5; 174/400] END max_depth=5, min_samples_leaf=2, n_estimators=4;, score=0.817 total time= 0.0s
[CV 2/5; 174/400] START max_depth=5, min_samples_leaf=2, n_estimators=4.........
[CV 2/5; 174/400] END max_depth=5, min_samples_leaf=2, n_estimators=4;, score=0.810 total time= 0.0s
[CV 3/5; 174/400] START max_depth=5, min_samples_leaf=2, n_estimators=4.........
[CV 3/5; 174/400] END max_depth=5, min_samples_leaf=2, n_estimators=4;, score=0.795 total time= 0.0s
[CV 4/5; 174/400] START max_depth=5, min_samples_leaf=2, n_estimators=4.........
[CV 4/5; 174/400] END max_depth=5, min_samples_leaf=2, n_estimators=4;, score=0.816 total time= 0.0s
[CV 5/5; 174/400] START max_depth=5, min_samples_leaf=2, n_estimators=4.........
[CV 5/5; 174/400] END max_depth=5, min_samples_leaf=2, n_estimators=4;, score=0.781 total time= 0.0s
[CV 1/5; 175/400] START max_depth=5, min_samples_leaf=2, n_estimators=5.........
[CV 1/5; 175/400] END max_depth=5, min_samples_leaf=2, n_estimators=5;, score=0.796 total time= 0.0s
[CV 2/5; 175/400] START max_depth=5, min_samples_leaf=2, n_estimators=5.........
[CV 2/5; 175/400] END max_depth=5, min_samples_leaf=2, n_estimators=5;, score=0.810 total time= 0.0s
[CV 3/5; 175/400] START max_depth=5, min_samples_leaf=2, n_estimators=5.........
[CV 3/5; 175/400] END max_depth=5, min_samples_leaf=2, n_estimators=5;, score=0.783 total time= 0.0s
[CV 4/5; 175/400] START max_depth=5, min_samples_leaf=2, n_estimators=5.........
[CV 4/5; 175/400] END max_depth=5, min_samples_leaf=2, n_estimators=5;, score=0.804 total time= 0.0s
[CV 5/5; 175/400] START max_depth=5, min_samples_leaf=2, n_estimators=5.........
[CV 5/5; 175/400] END max_depth=5, min_samples_leaf=2, n_estimators=5;, score=0.782 total time= 0.0s
[CV 1/5; 176/400] START max_depth=5, min_samples_leaf=2, n_estimators=6.........
[CV 1/5; 176/400] END max_depth=5, min_samples_leaf=2, n_estimators=6;, score=0.816 total time= 0.1s
[CV 2/5; 176/400] START max_depth=5, min_samples_leaf=2, n_estimators=6.........
[CV 2/5; 176/400] END max_depth=5, min_samples_leaf=2, n_estimators=6;, score=0.807 total time= 0.1s
[CV 3/5; 176/400] START max_depth=5, min_samples_leaf=2, n_estimators=6.........
[CV 3/5; 176/400] END max_depth=5, min_samples_leaf=2, n_estimators=6;, score=0.800 total time= 0.1s
[CV 4/5; 176/400] START max_depth=5, min_samples_leaf=2, n_estimators=6.........
[CV 4/5; 176/400] END max_depth=5, min_samples_leaf=2, n_estimators=6;, score=0.813 total time= 0.1s
[CV 5/5; 176/400] START max_depth=5, min_samples_leaf=2, n_estimators=6.........
[CV 5/5; 176/400] END max_depth=5, min_samples_leaf=2, n_estimators=6;, score=0.783 total time= 0.1s
[CV 1/5; 177/400] START max_depth=5, min_samples_leaf=2, n_estimators=7.........
[CV 1/5; 177/400] END max_depth=5, min_samples_leaf=2, n_estimators=7;, score=0.811 total time= 0.1s
[CV 2/5; 177/400] START max_depth=5, min_samples_leaf=2, n_estimators=7.........
[CV 2/5; 177/400] END max_depth=5, min_samples_leaf=2, n_estimators=7;, score=0.807 total time= 0.1s
[CV 3/5; 177/400] START max_depth=5, min_samples_leaf=2, n_estimators=7.........
[CV 3/5; 177/400] END max_depth=5, min_samples_leaf=2, n_estimators=7;, score=0.799 total time= 0.1s
[CV 4/5; 177/400] START max_depth=5, min_samples_leaf=2, n_estimators=7.........
[CV 4/5; 177/400] END max_depth=5, min_samples_leaf=2, n_estimators=7;, score=0.805 total time= 0.1s
[CV 5/5; 177/400] START max_depth=5, min_samples_leaf=2, n_estimators=7.........
[CV 5/5; 177/400] END max_depth=5, min_samples_leaf=2, n_estimators=7;, score=0.799 total time= 0.1s
[CV 1/5; 178/400] START max_depth=5, min_samples_leaf=2, n_estimators=8.........
[CV 1/5; 178/400] END max_depth=5, min_samples_leaf=2, n_estimators=8;, score=0.810 total time= 0.1s
[CV 2/5; 178/400] START max_depth=5, min_samples_leaf=2, n_estimators=8.........
[CV 2/5; 178/400] END max_depth=5, min_samples_leaf=2, n_estimators=8;, score=0.799 total time= 0.1s
[CV 3/5; 178/400] START max_depth=5, min_samples_leaf=2, n_estimators=8.........
[CV 3/5; 178/400] END max_depth=5, min_samples_leaf=2, n_estimators=8;, score=0.799 total time= 0.1s
[CV 4/5; 178/400] START max_depth=5, min_samples_leaf=2, n_estimators=8.........
[CV 4/5; 178/400] END max_depth=5, min_samples_leaf=2, n_estimators=8;, score=0.804 total time= 0.1s
[CV 5/5; 178/400] START max_depth=5, min_samples_leaf=2, n_estimators=8.........
[CV 5/5; 178/400] END max_depth=5, min_samples_leaf=2, n_estimators=8;, score=0.786 total time= 0.1s
[CV 1/5; 179/400] START max_depth=5, min_samples_leaf=2, n_estimators=9.........
[CV 1/5; 179/400] END max_depth=5, min_samples_leaf=2, n_estimators=9;, score=0.814 total time= 0.1s
[CV 2/5; 179/400] START max_depth=5, min_samples_leaf=2, n_estimators=9.........
[CV 2/5; 179/400] END max_depth=5, min_samples_leaf=2, n_estimators=9;, score=0.810 total time= 0.1s
[CV 3/5; 179/400] START max_depth=5, min_samples_leaf=2, n_estimators=9.........
[CV 3/5; 179/400] END max_depth=5, min_samples_leaf=2, n_estimators=9;, score=0.795 total time= 0.1s
[CV 4/5; 179/400] START max_depth=5, min_samples_leaf=2, n_estimators=9.........
[CV 4/5; 179/400] END max_depth=5, min_samples_leaf=2, n_estimators=9;, score=0.799 total time= 0.1s
[CV 5/5; 179/400] START max_depth=5, min_samples_leaf=2, n_estimators=9.........
[CV 5/5; 179/400] END max_depth=5, min_samples_leaf=2, n_estimators=9;, score=0.787 total time= 0.1s
[CV 1/5; 180/400] START max_depth=5, min_samples_leaf=2, n_estimators=10........
[CV 1/5; 180/400] END max_depth=5, min_samples_leaf=2, n_estimators=10;, score=0.811 total time= 0.1s
[CV 2/5; 180/400] START max_depth=5, min_samples_leaf=2, n_estimators=10........
[CV 2/5; 180/400] END max_depth=5, min_samples_leaf=2, n_estimators=10;, score=0.804 total time= 0.1s
[CV 3/5; 180/400] START max_depth=5, min_samples_leaf=2, n_estimators=10........
[CV 3/5; 180/400] END max_depth=5, min_samples_leaf=2, n_estimators=10;, score=0.797 total time= 0.1s
[CV 4/5; 180/400] START max_depth=5, min_samples_leaf=2, n_estimators=10........
[CV 4/5; 180/400] END max_depth=5, min_samples_leaf=2, n_estimators=10;, score=0.809 total time= 0.1s
[CV 5/5; 180/400] START max_depth=5, min_samples_leaf=2, n_estimators=10........
[CV 5/5; 180/400] END max_depth=5, min_samples_leaf=2, n_estimators=10;, score=0.795 total time= 0.1s
[CV 1/5; 181/400] START max_depth=5, min_samples_leaf=3, n_estimators=1.........
[CV 1/5; 181/400] END max_depth=5, min_samples_leaf=3, n_estimators=1;, score=0.725 total time= 0.0s
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[CV 2/5; 181/400] END max_depth=5, min_samples_leaf=3, n_estimators=1;, score=0.795 total time= 0.0s
[CV 3/5; 181/400] START max_depth=5, min_samples_leaf=3, n_estimators=1.........
[CV 3/5; 181/400] END max_depth=5, min_samples_leaf=3, n_estimators=1;, score=0.763 total time= 0.0s
[CV 4/5; 181/400] START max_depth=5, min_samples_leaf=3, n_estimators=1.........
[CV 4/5; 181/400] END max_depth=5, min_samples_leaf=3, n_estimators=1;, score=0.785 total time= 0.0s
[CV 5/5; 181/400] START max_depth=5, min_samples_leaf=3, n_estimators=1.........
[CV 5/5; 181/400] END max_depth=5, min_samples_leaf=3, n_estimators=1;, score=0.774 total time= 0.0s
[CV 1/5; 182/400] START max_depth=5, min_samples_leaf=3, n_estimators=2.........
[CV 1/5; 182/400] END max_depth=5, min_samples_leaf=3, n_estimators=2;, score=0.801 total time= 0.0s
[CV 2/5; 182/400] START max_depth=5, min_samples_leaf=3, n_estimators=2.........
[CV 2/5; 182/400] END max_depth=5, min_samples_leaf=3, n_estimators=2;, score=0.798 total time= 0.0s
[CV 3/5; 182/400] START max_depth=5, min_samples_leaf=3, n_estimators=2.........
[CV 3/5; 182/400] END max_depth=5, min_samples_leaf=3, n_estimators=2;, score=0.790 total time= 0.0s
[CV 4/5; 182/400] START max_depth=5, min_samples_leaf=3, n_estimators=2.........
[CV 4/5; 182/400] END max_depth=5, min_samples_leaf=3, n_estimators=2;, score=0.802 total time= 0.0s
[CV 5/5; 182/400] START max_depth=5, min_samples_leaf=3, n_estimators=2.........
[CV 5/5; 182/400] END max_depth=5, min_samples_leaf=3, n_estimators=2;, score=0.775 total time= 0.0s
[CV 1/5; 183/400] START max_depth=5, min_samples_leaf=3, n_estimators=3.........
[CV 1/5; 183/400] END max_depth=5, min_samples_leaf=3, n_estimators=3;, score=0.811 total time= 0.0s
[CV 2/5; 183/400] START max_depth=5, min_samples_leaf=3, n_estimators=3.........
[CV 2/5; 183/400] END max_depth=5, min_samples_leaf=3, n_estimators=3;, score=0.795 total time= 0.0s
[CV 3/5; 183/400] START max_depth=5, min_samples_leaf=3, n_estimators=3.........
[CV 3/5; 183/400] END max_depth=5, min_samples_leaf=3, n_estimators=3;, score=0.786 total time= 0.0s
[CV 4/5; 183/400] START max_depth=5, min_samples_leaf=3, n_estimators=3.........
[CV 4/5; 183/400] END max_depth=5, min_samples_leaf=3, n_estimators=3;, score=0.804 total time= 0.0s
[CV 5/5; 183/400] START max_depth=5, min_samples_leaf=3, n_estimators=3.........
[CV 5/5; 183/400] END max_depth=5, min_samples_leaf=3, n_estimators=3;, score=0.785 total time= 0.0s
[CV 1/5; 184/400] START max_depth=5, min_samples_leaf=3, n_estimators=4.........
[CV 1/5; 184/400] END max_depth=5, min_samples_leaf=3, n_estimators=4;, score=0.798 total time= 0.0s
[CV 2/5; 184/400] START max_depth=5, min_samples_leaf=3, n_estimators=4.........
[CV 2/5; 184/400] END max_depth=5, min_samples_leaf=3, n_estimators=4;, score=0.813 total time= 0.0s
[CV 3/5; 184/400] START max_depth=5, min_samples_leaf=3, n_estimators=4.........
[CV 3/5; 184/400] END max_depth=5, min_samples_leaf=3, n_estimators=4;, score=0.804 total time= 0.0s
[CV 4/5; 184/400] START max_depth=5, min_samples_leaf=3, n_estimators=4.........
[CV 4/5; 184/400] END max_depth=5, min_samples_leaf=3, n_estimators=4;, score=0.802 total time= 0.0s
[CV 5/5; 184/400] START max_depth=5, min_samples_leaf=3, n_estimators=4.........
[CV 5/5; 184/400] END max_depth=5, min_samples_leaf=3, n_estimators=4;, score=0.800 total time= 0.0s
[CV 1/5; 185/400] START max_depth=5, min_samples_leaf=3, n_estimators=5.........
[CV 1/5; 185/400] END max_depth=5, min_samples_leaf=3, n_estimators=5;, score=0.805 total time= 0.0s
[CV 2/5; 185/400] START max_depth=5, min_samples_leaf=3, n_estimators=5.........
[CV 2/5; 185/400] END max_depth=5, min_samples_leaf=3, n_estimators=5;, score=0.795 total time= 0.0s
[CV 3/5; 185/400] START max_depth=5, min_samples_leaf=3, n_estimators=5.........
[CV 3/5; 185/400] END max_depth=5, min_samples_leaf=3, n_estimators=5;, score=0.794 total time= 0.0s
[CV 4/5; 185/400] START max_depth=5, min_samples_leaf=3, n_estimators=5.........
[CV 4/5; 185/400] END max_depth=5, min_samples_leaf=3, n_estimators=5;, score=0.810 total time= 0.1s
[CV 5/5; 185/400] START max_depth=5, min_samples_leaf=3, n_estimators=5.........
[CV 5/5; 185/400] END max_depth=5, min_samples_leaf=3, n_estimators=5;, score=0.784 total time= 0.0s
[CV 1/5; 186/400] START max_depth=5, min_samples_leaf=3, n_estimators=6.........
[CV 1/5; 186/400] END max_depth=5, min_samples_leaf=3, n_estimators=6;, score=0.803 total time= 0.1s
[CV 2/5; 186/400] START max_depth=5, min_samples_leaf=3, n_estimators=6.........
[CV 2/5; 186/400] END max_depth=5, min_samples_leaf=3, n_estimators=6;, score=0.797 total time= 0.1s
[CV 3/5; 186/400] START max_depth=5, min_samples_leaf=3, n_estimators=6.........
[CV 3/5; 186/400] END max_depth=5, min_samples_leaf=3, n_estimators=6;, score=0.792 total time= 0.0s
[CV 4/5; 186/400] START max_depth=5, min_samples_leaf=3, n_estimators=6.........
[CV 4/5; 186/400] END max_depth=5, min_samples_leaf=3, n_estimators=6;, score=0.810 total time= 0.0s
[CV 5/5; 186/400] START max_depth=5, min_samples_leaf=3, n_estimators=6.........
[CV 5/5; 186/400] END max_depth=5, min_samples_leaf=3, n_estimators=6;, score=0.790 total time= 0.0s
[CV 1/5; 187/400] START max_depth=5, min_samples_leaf=3, n_estimators=7.........
[CV 1/5; 187/400] END max_depth=5, min_samples_leaf=3, n_estimators=7;, score=0.810 total time= 0.0s
[CV 2/5; 187/400] START max_depth=5, min_samples_leaf=3, n_estimators=7.........
[CV 2/5; 187/400] END max_depth=5, min_samples_leaf=3, n_estimators=7;, score=0.813 total time= 0.0s
[CV 3/5; 187/400] START max_depth=5, min_samples_leaf=3, n_estimators=7.........
[CV 3/5; 187/400] END max_depth=5, min_samples_leaf=3, n_estimators=7;, score=0.811 total time= 0.0s
[CV 4/5; 187/400] START max_depth=5, min_samples_leaf=3, n_estimators=7.........
[CV 4/5; 187/400] END max_depth=5, min_samples_leaf=3, n_estimators=7;, score=0.808 total time= 0.0s
[CV 5/5; 187/400] START max_depth=5, min_samples_leaf=3, n_estimators=7.........
[CV 5/5; 187/400] END max_depth=5, min_samples_leaf=3, n_estimators=7;, score=0.799 total time= 0.0s
[CV 1/5; 188/400] START max_depth=5, min_samples_leaf=3, n_estimators=8.........
[CV 1/5; 188/400] END max_depth=5, min_samples_leaf=3, n_estimators=8;, score=0.808 total time= 0.1s
[CV 2/5; 188/400] START max_depth=5, min_samples_leaf=3, n_estimators=8.........
[CV 2/5; 188/400] END max_depth=5, min_samples_leaf=3, n_estimators=8;, score=0.802 total time= 0.0s
[CV 3/5; 188/400] START max_depth=5, min_samples_leaf=3, n_estimators=8.........
[CV 3/5; 188/400] END max_depth=5, min_samples_leaf=3, n_estimators=8;, score=0.805 total time= 0.0s
[CV 4/5; 188/400] START max_depth=5, min_samples_leaf=3, n_estimators=8.........
[CV 4/5; 188/400] END max_depth=5, min_samples_leaf=3, n_estimators=8;, score=0.805 total time= 0.0s
[CV 5/5; 188/400] START max_depth=5, min_samples_leaf=3, n_estimators=8.........
[CV 5/5; 188/400] END max_depth=5, min_samples_leaf=3, n_estimators=8;, score=0.785 total time= 0.0s
[CV 1/5; 189/400] START max_depth=5, min_samples_leaf=3, n_estimators=9.........
[CV 1/5; 189/400] END max_depth=5, min_samples_leaf=3, n_estimators=9;, score=0.808 total time= 0.0s
[CV 2/5; 189/400] START max_depth=5, min_samples_leaf=3, n_estimators=9.........
[CV 2/5; 189/400] END max_depth=5, min_samples_leaf=3, n_estimators=9;, score=0.801 total time= 0.0s
[CV 3/5; 189/400] START max_depth=5, min_samples_leaf=3, n_estimators=9.........
[CV 3/5; 189/400] END max_depth=5, min_samples_leaf=3, n_estimators=9;, score=0.800 total time= 0.0s
[CV 4/5; 189/400] START max_depth=5, min_samples_leaf=3, n_estimators=9.........
[CV 4/5; 189/400] END max_depth=5, min_samples_leaf=3, n_estimators=9;, score=0.807 total time= 0.1s
[CV 5/5; 189/400] START max_depth=5, min_samples_leaf=3, n_estimators=9.........
[CV 5/5; 189/400] END max_depth=5, min_samples_leaf=3, n_estimators=9;, score=0.783 total time= 0.1s
[CV 1/5; 190/400] START max_depth=5, min_samples_leaf=3, n_estimators=10........
[CV 1/5; 190/400] END max_depth=5, min_samples_leaf=3, n_estimators=10;, score=0.805 total time= 0.1s
[CV 2/5; 190/400] START max_depth=5, min_samples_leaf=3, n_estimators=10........
[CV 2/5; 190/400] END max_depth=5, min_samples_leaf=3, n_estimators=10;, score=0.806 total time= 0.1s
[CV 3/5; 190/400] START max_depth=5, min_samples_leaf=3, n_estimators=10........
[CV 3/5; 190/400] END max_depth=5, min_samples_leaf=3, n_estimators=10;, score=0.793 total time= 0.1s
[CV 4/5; 190/400] START max_depth=5, min_samples_leaf=3, n_estimators=10........
[CV 4/5; 190/400] END max_depth=5, min_samples_leaf=3, n_estimators=10;, score=0.805 total time= 0.1s
[CV 5/5; 190/400] START max_depth=5, min_samples_leaf=3, n_estimators=10........
[CV 5/5; 190/400] END max_depth=5, min_samples_leaf=3, n_estimators=10;, score=0.788 total time= 0.1s
[CV 1/5; 191/400] START max_depth=5, min_samples_leaf=4, n_estimators=1.........
[CV 1/5; 191/400] END max_depth=5, min_samples_leaf=4, n_estimators=1;, score=0.758 total time= 0.0s
[CV 2/5; 191/400] START max_depth=5, min_samples_leaf=4, n_estimators=1.........
[CV 2/5; 191/400] END max_depth=5, min_samples_leaf=4, n_estimators=1;, score=0.784 total time= 0.0s
[CV 3/5; 191/400] START max_depth=5, min_samples_leaf=4, n_estimators=1.........
[CV 3/5; 191/400] END max_depth=5, min_samples_leaf=4, n_estimators=1;, score=0.782 total time= 0.0s
[CV 4/5; 191/400] START max_depth=5, min_samples_leaf=4, n_estimators=1.........
[CV 4/5; 191/400] END max_depth=5, min_samples_leaf=4, n_estimators=1;, score=0.780 total time= 0.0s
[CV 5/5; 191/400] START max_depth=5, min_samples_leaf=4, n_estimators=1.........
[CV 5/5; 191/400] END max_depth=5, min_samples_leaf=4, n_estimators=1;, score=0.760 total time= 0.0s
[CV 1/5; 192/400] START max_depth=5, min_samples_leaf=4, n_estimators=2.........
[CV 1/5; 192/400] END max_depth=5, min_samples_leaf=4, n_estimators=2;, score=0.807 total time= 0.0s
[CV 2/5; 192/400] START max_depth=5, min_samples_leaf=4, n_estimators=2.........
[CV 2/5; 192/400] END max_depth=5, min_samples_leaf=4, n_estimators=2;, score=0.791 total time= 0.0s
[CV 3/5; 192/400] START max_depth=5, min_samples_leaf=4, n_estimators=2.........
[CV 3/5; 192/400] END max_depth=5, min_samples_leaf=4, n_estimators=2;, score=0.799 total time= 0.0s
[CV 4/5; 192/400] START max_depth=5, min_samples_leaf=4, n_estimators=2.........
[CV 4/5; 192/400] END max_depth=5, min_samples_leaf=4, n_estimators=2;, score=0.799 total time= 0.0s
[CV 5/5; 192/400] START max_depth=5, min_samples_leaf=4, n_estimators=2.........
[CV 5/5; 192/400] END max_depth=5, min_samples_leaf=4, n_estimators=2;, score=0.783 total time= 0.0s
[CV 1/5; 193/400] START max_depth=5, min_samples_leaf=4, n_estimators=3.........
[CV 1/5; 193/400] END max_depth=5, min_samples_leaf=4, n_estimators=3;, score=0.805 total time= 0.0s
[CV 2/5; 193/400] START max_depth=5, min_samples_leaf=4, n_estimators=3.........
[CV 2/5; 193/400] END max_depth=5, min_samples_leaf=4, n_estimators=3;, score=0.817 total time= 0.0s
[CV 3/5; 193/400] START max_depth=5, min_samples_leaf=4, n_estimators=3.........
[CV 3/5; 193/400] END max_depth=5, min_samples_leaf=4, n_estimators=3;, score=0.804 total time= 0.0s
[CV 4/5; 193/400] START max_depth=5, min_samples_leaf=4, n_estimators=3.........
[CV 4/5; 193/400] END max_depth=5, min_samples_leaf=4, n_estimators=3;, score=0.808 total time= 0.0s
[CV 5/5; 193/400] START max_depth=5, min_samples_leaf=4, n_estimators=3.........
[CV 5/5; 193/400] END max_depth=5, min_samples_leaf=4, n_estimators=3;, score=0.802 total time= 0.0s
[CV 1/5; 194/400] START max_depth=5, min_samples_leaf=4, n_estimators=4.........
[CV 1/5; 194/400] END max_depth=5, min_samples_leaf=4, n_estimators=4;, score=0.802 total time= 0.0s
[CV 2/5; 194/400] START max_depth=5, min_samples_leaf=4, n_estimators=4.........
[CV 2/5; 194/400] END max_depth=5, min_samples_leaf=4, n_estimators=4;, score=0.805 total time= 0.0s
[CV 3/5; 194/400] START max_depth=5, min_samples_leaf=4, n_estimators=4.........
[CV 3/5; 194/400] END max_depth=5, min_samples_leaf=4, n_estimators=4;, score=0.783 total time= 0.0s
[CV 4/5; 194/400] START max_depth=5, min_samples_leaf=4, n_estimators=4.........
[CV 4/5; 194/400] END max_depth=5, min_samples_leaf=4, n_estimators=4;, score=0.795 total time= 0.0s
[CV 5/5; 194/400] START max_depth=5, min_samples_leaf=4, n_estimators=4.........
[CV 5/5; 194/400] END max_depth=5, min_samples_leaf=4, n_estimators=4;, score=0.796 total time= 0.0s
[CV 1/5; 195/400] START max_depth=5, min_samples_leaf=4, n_estimators=5.........
[CV 1/5; 195/400] END max_depth=5, min_samples_leaf=4, n_estimators=5;, score=0.815 total time= 0.0s
[CV 2/5; 195/400] START max_depth=5, min_samples_leaf=4, n_estimators=5.........
[CV 2/5; 195/400] END max_depth=5, min_samples_leaf=4, n_estimators=5;, score=0.806 total time= 0.0s
[CV 3/5; 195/400] START max_depth=5, min_samples_leaf=4, n_estimators=5.........
[CV 3/5; 195/400] END max_depth=5, min_samples_leaf=4, n_estimators=5;, score=0.813 total time= 0.0s
[CV 4/5; 195/400] START max_depth=5, min_samples_leaf=4, n_estimators=5.........
[CV 4/5; 195/400] END max_depth=5, min_samples_leaf=4, n_estimators=5;, score=0.803 total time= 0.0s
[CV 5/5; 195/400] START max_depth=5, min_samples_leaf=4, n_estimators=5.........
[CV 5/5; 195/400] END max_depth=5, min_samples_leaf=4, n_estimators=5;, score=0.791 total time= 0.0s
[CV 1/5; 196/400] START max_depth=5, min_samples_leaf=4, n_estimators=6.........
[CV 1/5; 196/400] END max_depth=5, min_samples_leaf=4, n_estimators=6;, score=0.813 total time= 0.0s
[CV 2/5; 196/400] START max_depth=5, min_samples_leaf=4, n_estimators=6.........
[CV 2/5; 196/400] END max_depth=5, min_samples_leaf=4, n_estimators=6;, score=0.802 total time= 0.0s
[CV 3/5; 196/400] START max_depth=5, min_samples_leaf=4, n_estimators=6.........
[CV 3/5; 196/400] END max_depth=5, min_samples_leaf=4, n_estimators=6;, score=0.801 total time= 0.0s
[CV 4/5; 196/400] START max_depth=5, min_samples_leaf=4, n_estimators=6.........
[CV 4/5; 196/400] END max_depth=5, min_samples_leaf=4, n_estimators=6;, score=0.807 total time= 0.0s
[CV 5/5; 196/400] START max_depth=5, min_samples_leaf=4, n_estimators=6.........
[CV 5/5; 196/400] END max_depth=5, min_samples_leaf=4, n_estimators=6;, score=0.794 total time= 0.0s
[CV 1/5; 197/400] START max_depth=5, min_samples_leaf=4, n_estimators=7.........
[CV 1/5; 197/400] END max_depth=5, min_samples_leaf=4, n_estimators=7;, score=0.807 total time= 0.0s
[CV 2/5; 197/400] START max_depth=5, min_samples_leaf=4, n_estimators=7.........
[CV 2/5; 197/400] END max_depth=5, min_samples_leaf=4, n_estimators=7;, score=0.806 total time= 0.0s
[CV 3/5; 197/400] START max_depth=5, min_samples_leaf=4, n_estimators=7.........
[CV 3/5; 197/400] END max_depth=5, min_samples_leaf=4, n_estimators=7;, score=0.802 total time= 0.0s
[CV 4/5; 197/400] START max_depth=5, min_samples_leaf=4, n_estimators=7.........
[CV 4/5; 197/400] END max_depth=5, min_samples_leaf=4, n_estimators=7;, score=0.817 total time= 0.0s
[CV 5/5; 197/400] START max_depth=5, min_samples_leaf=4, n_estimators=7.........
[CV 5/5; 197/400] END max_depth=5, min_samples_leaf=4, n_estimators=7;, score=0.800 total time= 0.0s
[CV 1/5; 198/400] START max_depth=5, min_samples_leaf=4, n_estimators=8.........
[CV 1/5; 198/400] END max_depth=5, min_samples_leaf=4, n_estimators=8;, score=0.822 total time= 0.1s
[CV 2/5; 198/400] START max_depth=5, min_samples_leaf=4, n_estimators=8.........
[CV 2/5; 198/400] END max_depth=5, min_samples_leaf=4, n_estimators=8;, score=0.812 total time= 0.0s
[CV 3/5; 198/400] START max_depth=5, min_samples_leaf=4, n_estimators=8.........
[CV 3/5; 198/400] END max_depth=5, min_samples_leaf=4, n_estimators=8;, score=0.795 total time= 0.0s
[CV 4/5; 198/400] START max_depth=5, min_samples_leaf=4, n_estimators=8.........
[CV 4/5; 198/400] END max_depth=5, min_samples_leaf=4, n_estimators=8;, score=0.802 total time= 0.0s
[CV 5/5; 198/400] START max_depth=5, min_samples_leaf=4, n_estimators=8.........
[CV 5/5; 198/400] END max_depth=5, min_samples_leaf=4, n_estimators=8;, score=0.787 total time= 0.0s
[CV 1/5; 199/400] START max_depth=5, min_samples_leaf=4, n_estimators=9.........
[CV 1/5; 199/400] END max_depth=5, min_samples_leaf=4, n_estimators=9;, score=0.812 total time= 0.1s
[CV 2/5; 199/400] START max_depth=5, min_samples_leaf=4, n_estimators=9.........
[CV 2/5; 199/400] END max_depth=5, min_samples_leaf=4, n_estimators=9;, score=0.808 total time= 0.1s
[CV 3/5; 199/400] START max_depth=5, min_samples_leaf=4, n_estimators=9.........
[CV 3/5; 199/400] END max_depth=5, min_samples_leaf=4, n_estimators=9;, score=0.791 total time= 0.1s
[CV 4/5; 199/400] START max_depth=5, min_samples_leaf=4, n_estimators=9.........
[CV 4/5; 199/400] END max_depth=5, min_samples_leaf=4, n_estimators=9;, score=0.807 total time= 0.0s
[CV 5/5; 199/400] START max_depth=5, min_samples_leaf=4, n_estimators=9.........
[CV 5/5; 199/400] END max_depth=5, min_samples_leaf=4, n_estimators=9;, score=0.783 total time= 0.1s
[CV 1/5; 200/400] START max_depth=5, min_samples_leaf=4, n_estimators=10........
[CV 1/5; 200/400] END max_depth=5, min_samples_leaf=4, n_estimators=10;, score=0.812 total time= 0.1s
[CV 2/5; 200/400] START max_depth=5, min_samples_leaf=4, n_estimators=10........
[CV 2/5; 200/400] END max_depth=5, min_samples_leaf=4, n_estimators=10;, score=0.805 total time= 0.1s
[CV 3/5; 200/400] START max_depth=5, min_samples_leaf=4, n_estimators=10........
[CV 3/5; 200/400] END max_depth=5, min_samples_leaf=4, n_estimators=10;, score=0.796 total time= 0.1s
[CV 4/5; 200/400] START max_depth=5, min_samples_leaf=4, n_estimators=10........
[CV 4/5; 200/400] END max_depth=5, min_samples_leaf=4, n_estimators=10;, score=0.803 total time= 0.1s
[CV 5/5; 200/400] START max_depth=5, min_samples_leaf=4, n_estimators=10........
[CV 5/5; 200/400] END max_depth=5, min_samples_leaf=4, n_estimators=10;, score=0.797 total time= 0.1s
[CV 1/5; 201/400] START max_depth=6, min_samples_leaf=1, n_estimators=1.........
[CV 1/5; 201/400] END max_depth=6, min_samples_leaf=1, n_estimators=1;, score=0.793 total time= 0.0s
[CV 2/5; 201/400] START max_depth=6, min_samples_leaf=1, n_estimators=1.........
[CV 2/5; 201/400] END max_depth=6, min_samples_leaf=1, n_estimators=1;, score=0.791 total time= 0.0s
[CV 3/5; 201/400] START max_depth=6, min_samples_leaf=1, n_estimators=1.........
[CV 3/5; 201/400] END max_depth=6, min_samples_leaf=1, n_estimators=1;, score=0.799 total time= 0.0s
[CV 4/5; 201/400] START max_depth=6, min_samples_leaf=1, n_estimators=1.........
[CV 4/5; 201/400] END max_depth=6, min_samples_leaf=1, n_estimators=1;, score=0.767 total time= 0.0s
[CV 5/5; 201/400] START max_depth=6, min_samples_leaf=1, n_estimators=1.........
[CV 5/5; 201/400] END max_depth=6, min_samples_leaf=1, n_estimators=1;, score=0.796 total time= 0.0s
[CV 1/5; 202/400] START max_depth=6, min_samples_leaf=1, n_estimators=2.........
[CV 1/5; 202/400] END max_depth=6, min_samples_leaf=1, n_estimators=2;, score=0.802 total time= 0.0s
[CV 2/5; 202/400] START max_depth=6, min_samples_leaf=1, n_estimators=2.........
[CV 2/5; 202/400] END max_depth=6, min_samples_leaf=1, n_estimators=2;, score=0.805 total time= 0.0s
[CV 3/5; 202/400] START max_depth=6, min_samples_leaf=1, n_estimators=2.........
[CV 3/5; 202/400] END max_depth=6, min_samples_leaf=1, n_estimators=2;, score=0.790 total time= 0.0s
[CV 4/5; 202/400] START max_depth=6, min_samples_leaf=1, n_estimators=2.........
[CV 4/5; 202/400] END max_depth=6, min_samples_leaf=1, n_estimators=2;, score=0.807 total time= 0.0s
[CV 5/5; 202/400] START max_depth=6, min_samples_leaf=1, n_estimators=2.........
[CV 5/5; 202/400] END max_depth=6, min_samples_leaf=1, n_estimators=2;, score=0.796 total time= 0.0s
[CV 1/5; 203/400] START max_depth=6, min_samples_leaf=1, n_estimators=3.........
[CV 1/5; 203/400] END max_depth=6, min_samples_leaf=1, n_estimators=3;, score=0.809 total time= 0.0s
[CV 2/5; 203/400] START max_depth=6, min_samples_leaf=1, n_estimators=3.........
[CV 2/5; 203/400] END max_depth=6, min_samples_leaf=1, n_estimators=3;, score=0.828 total time= 0.0s
[CV 3/5; 203/400] START max_depth=6, min_samples_leaf=1, n_estimators=3.........
[CV 3/5; 203/400] END max_depth=6, min_samples_leaf=1, n_estimators=3;, score=0.804 total time= 0.0s
[CV 4/5; 203/400] START max_depth=6, min_samples_leaf=1, n_estimators=3.........
[CV 4/5; 203/400] END max_depth=6, min_samples_leaf=1, n_estimators=3;, score=0.824 total time= 0.0s
[CV 5/5; 203/400] START max_depth=6, min_samples_leaf=1, n_estimators=3.........
[CV 5/5; 203/400] END max_depth=6, min_samples_leaf=1, n_estimators=3;, score=0.791 total time= 0.0s
[CV 1/5; 204/400] START max_depth=6, min_samples_leaf=1, n_estimators=4.........
[CV 1/5; 204/400] END max_depth=6, min_samples_leaf=1, n_estimators=4;, score=0.818 total time= 0.0s
[CV 2/5; 204/400] START max_depth=6, min_samples_leaf=1, n_estimators=4.........
[CV 2/5; 204/400] END max_depth=6, min_samples_leaf=1, n_estimators=4;, score=0.821 total time= 0.0s
[CV 3/5; 204/400] START max_depth=6, min_samples_leaf=1, n_estimators=4.........
[CV 3/5; 204/400] END max_depth=6, min_samples_leaf=1, n_estimators=4;, score=0.812 total time= 0.0s
[CV 4/5; 204/400] START max_depth=6, min_samples_leaf=1, n_estimators=4.........
[CV 4/5; 204/400] END max_depth=6, min_samples_leaf=1, n_estimators=4;, score=0.810 total time= 0.0s
[CV 5/5; 204/400] START max_depth=6, min_samples_leaf=1, n_estimators=4.........
[CV 5/5; 204/400] END max_depth=6, min_samples_leaf=1, n_estimators=4;, score=0.810 total time= 0.0s
[CV 1/5; 205/400] START max_depth=6, min_samples_leaf=1, n_estimators=5.........
[CV 1/5; 205/400] END max_depth=6, min_samples_leaf=1, n_estimators=5;, score=0.808 total time= 0.0s
[CV 2/5; 205/400] START max_depth=6, min_samples_leaf=1, n_estimators=5.........
[CV 2/5; 205/400] END max_depth=6, min_samples_leaf=1, n_estimators=5;, score=0.815 total time= 0.0s
[CV 3/5; 205/400] START max_depth=6, min_samples_leaf=1, n_estimators=5.........
[CV 3/5; 205/400] END max_depth=6, min_samples_leaf=1, n_estimators=5;, score=0.794 total time= 0.0s
[CV 4/5; 205/400] START max_depth=6, min_samples_leaf=1, n_estimators=5.........
[CV 4/5; 205/400] END max_depth=6, min_samples_leaf=1, n_estimators=5;, score=0.805 total time= 0.0s
[CV 5/5; 205/400] START max_depth=6, min_samples_leaf=1, n_estimators=5.........
[CV 5/5; 205/400] END max_depth=6, min_samples_leaf=1, n_estimators=5;, score=0.805 total time= 0.0s
[CV 1/5; 206/400] START max_depth=6, min_samples_leaf=1, n_estimators=6.........
[CV 1/5; 206/400] END max_depth=6, min_samples_leaf=1, n_estimators=6;, score=0.819 total time= 0.0s
[CV 2/5; 206/400] START max_depth=6, min_samples_leaf=1, n_estimators=6.........
[CV 2/5; 206/400] END max_depth=6, min_samples_leaf=1, n_estimators=6;, score=0.819 total time= 0.0s
[CV 3/5; 206/400] START max_depth=6, min_samples_leaf=1, n_estimators=6.........
[CV 3/5; 206/400] END max_depth=6, min_samples_leaf=1, n_estimators=6;, score=0.822 total time= 0.0s
[CV 4/5; 206/400] START max_depth=6, min_samples_leaf=1, n_estimators=6.........
[CV 4/5; 206/400] END max_depth=6, min_samples_leaf=1, n_estimators=6;, score=0.813 total time= 0.0s
[CV 5/5; 206/400] START max_depth=6, min_samples_leaf=1, n_estimators=6.........
[CV 5/5; 206/400] END max_depth=6, min_samples_leaf=1, n_estimators=6;, score=0.803 total time= 0.0s
[CV 1/5; 207/400] START max_depth=6, min_samples_leaf=1, n_estimators=7.........
[CV 1/5; 207/400] END max_depth=6, min_samples_leaf=1, n_estimators=7;, score=0.823 total time= 0.0s
[CV 2/5; 207/400] START max_depth=6, min_samples_leaf=1, n_estimators=7.........
[CV 2/5; 207/400] END max_depth=6, min_samples_leaf=1, n_estimators=7;, score=0.817 total time= 0.0s
[CV 3/5; 207/400] START max_depth=6, min_samples_leaf=1, n_estimators=7.........
[CV 3/5; 207/400] END max_depth=6, min_samples_leaf=1, n_estimators=7;, score=0.807 total time= 0.0s
[CV 4/5; 207/400] START max_depth=6, min_samples_leaf=1, n_estimators=7.........
[CV 4/5; 207/400] END max_depth=6, min_samples_leaf=1, n_estimators=7;, score=0.807 total time= 0.0s
[CV 5/5; 207/400] START max_depth=6, min_samples_leaf=1, n_estimators=7.........
[CV 5/5; 207/400] END max_depth=6, min_samples_leaf=1, n_estimators=7;, score=0.805 total time= 0.1s
[CV 1/5; 208/400] START max_depth=6, min_samples_leaf=1, n_estimators=8.........
[CV 1/5; 208/400] END max_depth=6, min_samples_leaf=1, n_estimators=8;, score=0.820 total time= 0.1s
[CV 2/5; 208/400] START max_depth=6, min_samples_leaf=1, n_estimators=8.........
[CV 2/5; 208/400] END max_depth=6, min_samples_leaf=1, n_estimators=8;, score=0.830 total time= 0.1s
[CV 3/5; 208/400] START max_depth=6, min_samples_leaf=1, n_estimators=8.........
[CV 3/5; 208/400] END max_depth=6, min_samples_leaf=1, n_estimators=8;, score=0.815 total time= 0.1s
[CV 4/5; 208/400] START max_depth=6, min_samples_leaf=1, n_estimators=8.........
[CV 4/5; 208/400] END max_depth=6, min_samples_leaf=1, n_estimators=8;, score=0.811 total time= 0.1s
[CV 5/5; 208/400] START max_depth=6, min_samples_leaf=1, n_estimators=8.........
[CV 5/5; 208/400] END max_depth=6, min_samples_leaf=1, n_estimators=8;, score=0.819 total time= 0.1s
[CV 1/5; 209/400] START max_depth=6, min_samples_leaf=1, n_estimators=9.........
[CV 1/5; 209/400] END max_depth=6, min_samples_leaf=1, n_estimators=9;, score=0.824 total time= 0.1s
[CV 2/5; 209/400] START max_depth=6, min_samples_leaf=1, n_estimators=9.........
[CV 2/5; 209/400] END max_depth=6, min_samples_leaf=1, n_estimators=9;, score=0.821 total time= 0.1s
[CV 3/5; 209/400] START max_depth=6, min_samples_leaf=1, n_estimators=9.........
[CV 3/5; 209/400] END max_depth=6, min_samples_leaf=1, n_estimators=9;, score=0.810 total time= 0.1s
[CV 4/5; 209/400] START max_depth=6, min_samples_leaf=1, n_estimators=9.........
[CV 4/5; 209/400] END max_depth=6, min_samples_leaf=1, n_estimators=9;, score=0.818 total time= 0.1s
[CV 5/5; 209/400] START max_depth=6, min_samples_leaf=1, n_estimators=9.........
[CV 5/5; 209/400] END max_depth=6, min_samples_leaf=1, n_estimators=9;, score=0.810 total time= 0.1s
[CV 1/5; 210/400] START max_depth=6, min_samples_leaf=1, n_estimators=10........
[CV 1/5; 210/400] END max_depth=6, min_samples_leaf=1, n_estimators=10;, score=0.827 total time= 0.1s
[CV 2/5; 210/400] START max_depth=6, min_samples_leaf=1, n_estimators=10........
[CV 2/5; 210/400] END max_depth=6, min_samples_leaf=1, n_estimators=10;, score=0.832 total time= 0.1s
[CV 3/5; 210/400] START max_depth=6, min_samples_leaf=1, n_estimators=10........
[CV 3/5; 210/400] END max_depth=6, min_samples_leaf=1, n_estimators=10;, score=0.807 total time= 0.1s
[CV 4/5; 210/400] START max_depth=6, min_samples_leaf=1, n_estimators=10........
[CV 4/5; 210/400] END max_depth=6, min_samples_leaf=1, n_estimators=10;, score=0.821 total time= 0.1s
[CV 5/5; 210/400] START max_depth=6, min_samples_leaf=1, n_estimators=10........
[CV 5/5; 210/400] END max_depth=6, min_samples_leaf=1, n_estimators=10;, score=0.798 total time= 0.1s
[CV 1/5; 211/400] START max_depth=6, min_samples_leaf=2, n_estimators=1.........
[CV 1/5; 211/400] END max_depth=6, min_samples_leaf=2, n_estimators=1;, score=0.759 total time= 0.0s
[CV 2/5; 211/400] START max_depth=6, min_samples_leaf=2, n_estimators=1.........
[CV 2/5; 211/400] END max_depth=6, min_samples_leaf=2, n_estimators=1;, score=0.807 total time= 0.0s
[CV 3/5; 211/400] START max_depth=6, min_samples_leaf=2, n_estimators=1.........
[CV 3/5; 211/400] END max_depth=6, min_samples_leaf=2, n_estimators=1;, score=0.782 total time= 0.0s
[CV 4/5; 211/400] START max_depth=6, min_samples_leaf=2, n_estimators=1.........
[CV 4/5; 211/400] END max_depth=6, min_samples_leaf=2, n_estimators=1;, score=0.768 total time= 0.0s
[CV 5/5; 211/400] START max_depth=6, min_samples_leaf=2, n_estimators=1.........
[CV 5/5; 211/400] END max_depth=6, min_samples_leaf=2, n_estimators=1;, score=0.805 total time= 0.0s
[CV 1/5; 212/400] START max_depth=6, min_samples_leaf=2, n_estimators=2.........
[CV 1/5; 212/400] END max_depth=6, min_samples_leaf=2, n_estimators=2;, score=0.810 total time= 0.0s
[CV 2/5; 212/400] START max_depth=6, min_samples_leaf=2, n_estimators=2.........
[CV 2/5; 212/400] END max_depth=6, min_samples_leaf=2, n_estimators=2;, score=0.796 total time= 0.0s
[CV 3/5; 212/400] START max_depth=6, min_samples_leaf=2, n_estimators=2.........
[CV 3/5; 212/400] END max_depth=6, min_samples_leaf=2, n_estimators=2;, score=0.810 total time= 0.0s
[CV 4/5; 212/400] START max_depth=6, min_samples_leaf=2, n_estimators=2.........
[CV 4/5; 212/400] END max_depth=6, min_samples_leaf=2, n_estimators=2;, score=0.794 total time= 0.0s
[CV 5/5; 212/400] START max_depth=6, min_samples_leaf=2, n_estimators=2.........
[CV 5/5; 212/400] END max_depth=6, min_samples_leaf=2, n_estimators=2;, score=0.799 total time= 0.0s
[CV 1/5; 213/400] START max_depth=6, min_samples_leaf=2, n_estimators=3.........
[CV 1/5; 213/400] END max_depth=6, min_samples_leaf=2, n_estimators=3;, score=0.813 total time= 0.0s
[CV 2/5; 213/400] START max_depth=6, min_samples_leaf=2, n_estimators=3.........
[CV 2/5; 213/400] END max_depth=6, min_samples_leaf=2, n_estimators=3;, score=0.818 total time= 0.0s
[CV 3/5; 213/400] START max_depth=6, min_samples_leaf=2, n_estimators=3.........
[CV 3/5; 213/400] END max_depth=6, min_samples_leaf=2, n_estimators=3;, score=0.819 total time= 0.0s
[CV 4/5; 213/400] START max_depth=6, min_samples_leaf=2, n_estimators=3.........
[CV 4/5; 213/400] END max_depth=6, min_samples_leaf=2, n_estimators=3;, score=0.810 total time= 0.0s
[CV 5/5; 213/400] START max_depth=6, min_samples_leaf=2, n_estimators=3.........
[CV 5/5; 213/400] END max_depth=6, min_samples_leaf=2, n_estimators=3;, score=0.806 total time= 0.0s
[CV 1/5; 214/400] START max_depth=6, min_samples_leaf=2, n_estimators=4.........
[CV 1/5; 214/400] END max_depth=6, min_samples_leaf=2, n_estimators=4;, score=0.814 total time= 0.0s
[CV 2/5; 214/400] START max_depth=6, min_samples_leaf=2, n_estimators=4.........
[CV 2/5; 214/400] END max_depth=6, min_samples_leaf=2, n_estimators=4;, score=0.821 total time= 0.0s
[CV 3/5; 214/400] START max_depth=6, min_samples_leaf=2, n_estimators=4.........
[CV 3/5; 214/400] END max_depth=6, min_samples_leaf=2, n_estimators=4;, score=0.814 total time= 0.0s
[CV 4/5; 214/400] START max_depth=6, min_samples_leaf=2, n_estimators=4.........
[CV 4/5; 214/400] END max_depth=6, min_samples_leaf=2, n_estimators=4;, score=0.816 total time= 0.0s
[CV 5/5; 214/400] START max_depth=6, min_samples_leaf=2, n_estimators=4.........
[CV 5/5; 214/400] END max_depth=6, min_samples_leaf=2, n_estimators=4;, score=0.808 total time= 0.0s
[CV 1/5; 215/400] START max_depth=6, min_samples_leaf=2, n_estimators=5.........
[CV 1/5; 215/400] END max_depth=6, min_samples_leaf=2, n_estimators=5;, score=0.816 total time= 0.0s
[CV 2/5; 215/400] START max_depth=6, min_samples_leaf=2, n_estimators=5.........
[CV 2/5; 215/400] END max_depth=6, min_samples_leaf=2, n_estimators=5;, score=0.813 total time= 0.0s
[CV 3/5; 215/400] START max_depth=6, min_samples_leaf=2, n_estimators=5.........
[CV 3/5; 215/400] END max_depth=6, min_samples_leaf=2, n_estimators=5;, score=0.816 total time= 0.0s
[CV 4/5; 215/400] START max_depth=6, min_samples_leaf=2, n_estimators=5.........
[CV 4/5; 215/400] END max_depth=6, min_samples_leaf=2, n_estimators=5;, score=0.819 total time= 0.0s
[CV 5/5; 215/400] START max_depth=6, min_samples_leaf=2, n_estimators=5.........
[CV 5/5; 215/400] END max_depth=6, min_samples_leaf=2, n_estimators=5;, score=0.801 total time= 0.0s
[CV 1/5; 216/400] START max_depth=6, min_samples_leaf=2, n_estimators=6.........
[CV 1/5; 216/400] END max_depth=6, min_samples_leaf=2, n_estimators=6;, score=0.816 total time= 0.0s
[CV 2/5; 216/400] START max_depth=6, min_samples_leaf=2, n_estimators=6.........
[CV 2/5; 216/400] END max_depth=6, min_samples_leaf=2, n_estimators=6;, score=0.818 total time= 0.0s
[CV 3/5; 216/400] START max_depth=6, min_samples_leaf=2, n_estimators=6.........
[CV 3/5; 216/400] END max_depth=6, min_samples_leaf=2, n_estimators=6;, score=0.799 total time= 0.0s
[CV 4/5; 216/400] START max_depth=6, min_samples_leaf=2, n_estimators=6.........
[CV 4/5; 216/400] END max_depth=6, min_samples_leaf=2, n_estimators=6;, score=0.819 total time= 0.0s
[CV 5/5; 216/400] START max_depth=6, min_samples_leaf=2, n_estimators=6.........
[CV 5/5; 216/400] END max_depth=6, min_samples_leaf=2, n_estimators=6;, score=0.824 total time= 0.0s
[CV 1/5; 217/400] START max_depth=6, min_samples_leaf=2, n_estimators=7.........
[CV 1/5; 217/400] END max_depth=6, min_samples_leaf=2, n_estimators=7;, score=0.824 total time= 0.1s
[CV 2/5; 217/400] START max_depth=6, min_samples_leaf=2, n_estimators=7.........
[CV 2/5; 217/400] END max_depth=6, min_samples_leaf=2, n_estimators=7;, score=0.808 total time= 0.0s
[CV 3/5; 217/400] START max_depth=6, min_samples_leaf=2, n_estimators=7.........
[CV 3/5; 217/400] END max_depth=6, min_samples_leaf=2, n_estimators=7;, score=0.817 total time= 0.0s
[CV 4/5; 217/400] START max_depth=6, min_samples_leaf=2, n_estimators=7.........
[CV 4/5; 217/400] END max_depth=6, min_samples_leaf=2, n_estimators=7;, score=0.811 total time= 0.0s
[CV 5/5; 217/400] START max_depth=6, min_samples_leaf=2, n_estimators=7.........
[CV 5/5; 217/400] END max_depth=6, min_samples_leaf=2, n_estimators=7;, score=0.813 total time= 0.0s
[CV 1/5; 218/400] START max_depth=6, min_samples_leaf=2, n_estimators=8.........
[CV 1/5; 218/400] END max_depth=6, min_samples_leaf=2, n_estimators=8;, score=0.812 total time= 0.0s
[CV 2/5; 218/400] START max_depth=6, min_samples_leaf=2, n_estimators=8.........
[CV 2/5; 218/400] END max_depth=6, min_samples_leaf=2, n_estimators=8;, score=0.828 total time= 0.0s
[CV 3/5; 218/400] START max_depth=6, min_samples_leaf=2, n_estimators=8.........
[CV 3/5; 218/400] END max_depth=6, min_samples_leaf=2, n_estimators=8;, score=0.832 total time= 0.1s
[CV 4/5; 218/400] START max_depth=6, min_samples_leaf=2, n_estimators=8.........
[CV 4/5; 218/400] END max_depth=6, min_samples_leaf=2, n_estimators=8;, score=0.824 total time= 0.1s
[CV 5/5; 218/400] START max_depth=6, min_samples_leaf=2, n_estimators=8.........
[CV 5/5; 218/400] END max_depth=6, min_samples_leaf=2, n_estimators=8;, score=0.803 total time= 0.0s
[CV 1/5; 219/400] START max_depth=6, min_samples_leaf=2, n_estimators=9.........
[CV 1/5; 219/400] END max_depth=6, min_samples_leaf=2, n_estimators=9;, score=0.819 total time= 0.1s
[CV 2/5; 219/400] START max_depth=6, min_samples_leaf=2, n_estimators=9.........
[CV 2/5; 219/400] END max_depth=6, min_samples_leaf=2, n_estimators=9;, score=0.822 total time= 0.1s
[CV 3/5; 219/400] START max_depth=6, min_samples_leaf=2, n_estimators=9.........
[CV 3/5; 219/400] END max_depth=6, min_samples_leaf=2, n_estimators=9;, score=0.807 total time= 0.1s
[CV 4/5; 219/400] START max_depth=6, min_samples_leaf=2, n_estimators=9.........
[CV 4/5; 219/400] END max_depth=6, min_samples_leaf=2, n_estimators=9;, score=0.814 total time= 0.1s
[CV 5/5; 219/400] START max_depth=6, min_samples_leaf=2, n_estimators=9.........
[CV 5/5; 219/400] END max_depth=6, min_samples_leaf=2, n_estimators=9;, score=0.805 total time= 0.1s
[CV 1/5; 220/400] START max_depth=6, min_samples_leaf=2, n_estimators=10........
[CV 1/5; 220/400] END max_depth=6, min_samples_leaf=2, n_estimators=10;, score=0.814 total time= 0.1s
[CV 2/5; 220/400] START max_depth=6, min_samples_leaf=2, n_estimators=10........
[CV 2/5; 220/400] END max_depth=6, min_samples_leaf=2, n_estimators=10;, score=0.807 total time= 0.1s
[CV 3/5; 220/400] START max_depth=6, min_samples_leaf=2, n_estimators=10........
[CV 3/5; 220/400] END max_depth=6, min_samples_leaf=2, n_estimators=10;, score=0.816 total time= 0.1s
[CV 4/5; 220/400] START max_depth=6, min_samples_leaf=2, n_estimators=10........
[CV 4/5; 220/400] END max_depth=6, min_samples_leaf=2, n_estimators=10;, score=0.816 total time= 0.1s
[CV 5/5; 220/400] START max_depth=6, min_samples_leaf=2, n_estimators=10........
[CV 5/5; 220/400] END max_depth=6, min_samples_leaf=2, n_estimators=10;, score=0.802 total time= 0.1s
[CV 1/5; 221/400] START max_depth=6, min_samples_leaf=3, n_estimators=1.........
[CV 1/5; 221/400] END max_depth=6, min_samples_leaf=3, n_estimators=1;, score=0.800 total time= 0.0s
[CV 2/5; 221/400] START max_depth=6, min_samples_leaf=3, n_estimators=1.........
[CV 2/5; 221/400] END max_depth=6, min_samples_leaf=3, n_estimators=1;, score=0.793 total time= 0.0s
[CV 3/5; 221/400] START max_depth=6, min_samples_leaf=3, n_estimators=1.........
[CV 3/5; 221/400] END max_depth=6, min_samples_leaf=3, n_estimators=1;, score=0.785 total time= 0.0s
[CV 4/5; 221/400] START max_depth=6, min_samples_leaf=3, n_estimators=1.........
[CV 4/5; 221/400] END max_depth=6, min_samples_leaf=3, n_estimators=1;, score=0.783 total time= 0.0s
[CV 5/5; 221/400] START max_depth=6, min_samples_leaf=3, n_estimators=1.........
[CV 5/5; 221/400] END max_depth=6, min_samples_leaf=3, n_estimators=1;, score=0.772 total time= 0.0s
[CV 1/5; 222/400] START max_depth=6, min_samples_leaf=3, n_estimators=2.........
[CV 1/5; 222/400] END max_depth=6, min_samples_leaf=3, n_estimators=2;, score=0.815 total time= 0.0s
[CV 2/5; 222/400] START max_depth=6, min_samples_leaf=3, n_estimators=2.........
[CV 2/5; 222/400] END max_depth=6, min_samples_leaf=3, n_estimators=2;, score=0.815 total time= 0.0s
[CV 3/5; 222/400] START max_depth=6, min_samples_leaf=3, n_estimators=2.........
[CV 3/5; 222/400] END max_depth=6, min_samples_leaf=3, n_estimators=2;, score=0.811 total time= 0.0s
[CV 4/5; 222/400] START max_depth=6, min_samples_leaf=3, n_estimators=2.........
[CV 4/5; 222/400] END max_depth=6, min_samples_leaf=3, n_estimators=2;, score=0.809 total time= 0.0s
[CV 5/5; 222/400] START max_depth=6, min_samples_leaf=3, n_estimators=2.........
[CV 5/5; 222/400] END max_depth=6, min_samples_leaf=3, n_estimators=2;, score=0.810 total time= 0.0s
[CV 1/5; 223/400] START max_depth=6, min_samples_leaf=3, n_estimators=3.........
[CV 1/5; 223/400] END max_depth=6, min_samples_leaf=3, n_estimators=3;, score=0.808 total time= 0.0s
[CV 2/5; 223/400] START max_depth=6, min_samples_leaf=3, n_estimators=3.........
[CV 2/5; 223/400] END max_depth=6, min_samples_leaf=3, n_estimators=3;, score=0.812 total time= 0.0s
[CV 3/5; 223/400] START max_depth=6, min_samples_leaf=3, n_estimators=3.........
[CV 3/5; 223/400] END max_depth=6, min_samples_leaf=3, n_estimators=3;, score=0.795 total time= 0.0s
[CV 4/5; 223/400] START max_depth=6, min_samples_leaf=3, n_estimators=3.........
[CV 4/5; 223/400] END max_depth=6, min_samples_leaf=3, n_estimators=3;, score=0.810 total time= 0.0s
[CV 5/5; 223/400] START max_depth=6, min_samples_leaf=3, n_estimators=3.........
[CV 5/5; 223/400] END max_depth=6, min_samples_leaf=3, n_estimators=3;, score=0.816 total time= 0.0s
[CV 1/5; 224/400] START max_depth=6, min_samples_leaf=3, n_estimators=4.........
[CV 1/5; 224/400] END max_depth=6, min_samples_leaf=3, n_estimators=4;, score=0.819 total time= 0.0s
[CV 2/5; 224/400] START max_depth=6, min_samples_leaf=3, n_estimators=4.........
[CV 2/5; 224/400] END max_depth=6, min_samples_leaf=3, n_estimators=4;, score=0.802 total time= 0.0s
[CV 3/5; 224/400] START max_depth=6, min_samples_leaf=3, n_estimators=4.........
[CV 3/5; 224/400] END max_depth=6, min_samples_leaf=3, n_estimators=4;, score=0.816 total time= 0.0s
[CV 4/5; 224/400] START max_depth=6, min_samples_leaf=3, n_estimators=4.........
[CV 4/5; 224/400] END max_depth=6, min_samples_leaf=3, n_estimators=4;, score=0.808 total time= 0.0s
[CV 5/5; 224/400] START max_depth=6, min_samples_leaf=3, n_estimators=4.........
[CV 5/5; 224/400] END max_depth=6, min_samples_leaf=3, n_estimators=4;, score=0.805 total time= 0.0s
[CV 1/5; 225/400] START max_depth=6, min_samples_leaf=3, n_estimators=5.........
[CV 1/5; 225/400] END max_depth=6, min_samples_leaf=3, n_estimators=5;, score=0.816 total time= 0.0s
[CV 2/5; 225/400] START max_depth=6, min_samples_leaf=3, n_estimators=5.........
[CV 2/5; 225/400] END max_depth=6, min_samples_leaf=3, n_estimators=5;, score=0.825 total time= 0.0s
[CV 3/5; 225/400] START max_depth=6, min_samples_leaf=3, n_estimators=5.........
[CV 3/5; 225/400] END max_depth=6, min_samples_leaf=3, n_estimators=5;, score=0.812 total time= 0.0s
[CV 4/5; 225/400] START max_depth=6, min_samples_leaf=3, n_estimators=5.........
[CV 4/5; 225/400] END max_depth=6, min_samples_leaf=3, n_estimators=5;, score=0.816 total time= 0.0s
[CV 5/5; 225/400] START max_depth=6, min_samples_leaf=3, n_estimators=5.........
[CV 5/5; 225/400] END max_depth=6, min_samples_leaf=3, n_estimators=5;, score=0.801 total time= 0.0s
[CV 1/5; 226/400] START max_depth=6, min_samples_leaf=3, n_estimators=6.........
[CV 1/5; 226/400] END max_depth=6, min_samples_leaf=3, n_estimators=6;, score=0.821 total time= 0.0s
[CV 2/5; 226/400] START max_depth=6, min_samples_leaf=3, n_estimators=6.........
[CV 2/5; 226/400] END max_depth=6, min_samples_leaf=3, n_estimators=6;, score=0.817 total time= 0.0s
[CV 3/5; 226/400] START max_depth=6, min_samples_leaf=3, n_estimators=6.........
[CV 3/5; 226/400] END max_depth=6, min_samples_leaf=3, n_estimators=6;, score=0.830 total time= 0.0s
[CV 4/5; 226/400] START max_depth=6, min_samples_leaf=3, n_estimators=6.........
[CV 4/5; 226/400] END max_depth=6, min_samples_leaf=3, n_estimators=6;, score=0.810 total time= 0.0s
[CV 5/5; 226/400] START max_depth=6, min_samples_leaf=3, n_estimators=6.........
[CV 5/5; 226/400] END max_depth=6, min_samples_leaf=3, n_estimators=6;, score=0.802 total time= 0.0s
[CV 1/5; 227/400] START max_depth=6, min_samples_leaf=3, n_estimators=7.........
[CV 1/5; 227/400] END max_depth=6, min_samples_leaf=3, n_estimators=7;, score=0.816 total time= 0.0s
[CV 2/5; 227/400] START max_depth=6, min_samples_leaf=3, n_estimators=7.........
[CV 2/5; 227/400] END max_depth=6, min_samples_leaf=3, n_estimators=7;, score=0.824 total time= 0.1s
[CV 3/5; 227/400] START max_depth=6, min_samples_leaf=3, n_estimators=7.........
[CV 3/5; 227/400] END max_depth=6, min_samples_leaf=3, n_estimators=7;, score=0.813 total time= 0.1s
[CV 4/5; 227/400] START max_depth=6, min_samples_leaf=3, n_estimators=7.........
[CV 4/5; 227/400] END max_depth=6, min_samples_leaf=3, n_estimators=7;, score=0.820 total time= 0.0s
[CV 5/5; 227/400] START max_depth=6, min_samples_leaf=3, n_estimators=7.........
[CV 5/5; 227/400] END max_depth=6, min_samples_leaf=3, n_estimators=7;, score=0.793 total time= 0.1s
[CV 1/5; 228/400] START max_depth=6, min_samples_leaf=3, n_estimators=8.........
[CV 1/5; 228/400] END max_depth=6, min_samples_leaf=3, n_estimators=8;, score=0.828 total time= 0.1s
[CV 2/5; 228/400] START max_depth=6, min_samples_leaf=3, n_estimators=8.........
[CV 2/5; 228/400] END max_depth=6, min_samples_leaf=3, n_estimators=8;, score=0.813 total time= 0.1s
[CV 3/5; 228/400] START max_depth=6, min_samples_leaf=3, n_estimators=8.........
[CV 3/5; 228/400] END max_depth=6, min_samples_leaf=3, n_estimators=8;, score=0.813 total time= 0.1s
[CV 4/5; 228/400] START max_depth=6, min_samples_leaf=3, n_estimators=8.........
[CV 4/5; 228/400] END max_depth=6, min_samples_leaf=3, n_estimators=8;, score=0.813 total time= 0.1s
[CV 5/5; 228/400] START max_depth=6, min_samples_leaf=3, n_estimators=8.........
[CV 5/5; 228/400] END max_depth=6, min_samples_leaf=3, n_estimators=8;, score=0.808 total time= 0.1s
[CV 1/5; 229/400] START max_depth=6, min_samples_leaf=3, n_estimators=9.........
[CV 1/5; 229/400] END max_depth=6, min_samples_leaf=3, n_estimators=9;, score=0.820 total time= 0.1s
[CV 2/5; 229/400] START max_depth=6, min_samples_leaf=3, n_estimators=9.........
[CV 2/5; 229/400] END max_depth=6, min_samples_leaf=3, n_estimators=9;, score=0.821 total time= 0.1s
[CV 3/5; 229/400] START max_depth=6, min_samples_leaf=3, n_estimators=9.........
[CV 3/5; 229/400] END max_depth=6, min_samples_leaf=3, n_estimators=9;, score=0.821 total time= 0.1s
[CV 4/5; 229/400] START max_depth=6, min_samples_leaf=3, n_estimators=9.........
[CV 4/5; 229/400] END max_depth=6, min_samples_leaf=3, n_estimators=9;, score=0.806 total time= 0.1s
[CV 5/5; 229/400] START max_depth=6, min_samples_leaf=3, n_estimators=9.........
[CV 5/5; 229/400] END max_depth=6, min_samples_leaf=3, n_estimators=9;, score=0.810 total time= 0.1s
[CV 1/5; 230/400] START max_depth=6, min_samples_leaf=3, n_estimators=10........
[CV 1/5; 230/400] END max_depth=6, min_samples_leaf=3, n_estimators=10;, score=0.832 total time= 0.1s
[CV 2/5; 230/400] START max_depth=6, min_samples_leaf=3, n_estimators=10........
[CV 2/5; 230/400] END max_depth=6, min_samples_leaf=3, n_estimators=10;, score=0.825 total time= 0.1s
[CV 3/5; 230/400] START max_depth=6, min_samples_leaf=3, n_estimators=10........
[CV 3/5; 230/400] END max_depth=6, min_samples_leaf=3, n_estimators=10;, score=0.816 total time= 0.1s
[CV 4/5; 230/400] START max_depth=6, min_samples_leaf=3, n_estimators=10........
[CV 4/5; 230/400] END max_depth=6, min_samples_leaf=3, n_estimators=10;, score=0.807 total time= 0.1s
[CV 5/5; 230/400] START max_depth=6, min_samples_leaf=3, n_estimators=10........
[CV 5/5; 230/400] END max_depth=6, min_samples_leaf=3, n_estimators=10;, score=0.809 total time= 0.1s
[CV 1/5; 231/400] START max_depth=6, min_samples_leaf=4, n_estimators=1.........
[CV 1/5; 231/400] END max_depth=6, min_samples_leaf=4, n_estimators=1;, score=0.735 total time= 0.0s
[CV 2/5; 231/400] START max_depth=6, min_samples_leaf=4, n_estimators=1.........
[CV 2/5; 231/400] END max_depth=6, min_samples_leaf=4, n_estimators=1;, score=0.790 total time= 0.0s
[CV 3/5; 231/400] START max_depth=6, min_samples_leaf=4, n_estimators=1.........
[CV 3/5; 231/400] END max_depth=6, min_samples_leaf=4, n_estimators=1;, score=0.772 total time= 0.0s
[CV 4/5; 231/400] START max_depth=6, min_samples_leaf=4, n_estimators=1.........
[CV 4/5; 231/400] END max_depth=6, min_samples_leaf=4, n_estimators=1;, score=0.812 total time= 0.0s
[CV 5/5; 231/400] START max_depth=6, min_samples_leaf=4, n_estimators=1.........
[CV 5/5; 231/400] END max_depth=6, min_samples_leaf=4, n_estimators=1;, score=0.775 total time= 0.0s
[CV 1/5; 232/400] START max_depth=6, min_samples_leaf=4, n_estimators=2.........
[CV 1/5; 232/400] END max_depth=6, min_samples_leaf=4, n_estimators=2;, score=0.811 total time= 0.0s
[CV 2/5; 232/400] START max_depth=6, min_samples_leaf=4, n_estimators=2.........
[CV 2/5; 232/400] END max_depth=6, min_samples_leaf=4, n_estimators=2;, score=0.805 total time= 0.0s
[CV 3/5; 232/400] START max_depth=6, min_samples_leaf=4, n_estimators=2.........
[CV 3/5; 232/400] END max_depth=6, min_samples_leaf=4, n_estimators=2;, score=0.809 total time= 0.0s
[CV 4/5; 232/400] START max_depth=6, min_samples_leaf=4, n_estimators=2.........
[CV 4/5; 232/400] END max_depth=6, min_samples_leaf=4, n_estimators=2;, score=0.793 total time= 0.0s
[CV 5/5; 232/400] START max_depth=6, min_samples_leaf=4, n_estimators=2.........
[CV 5/5; 232/400] END max_depth=6, min_samples_leaf=4, n_estimators=2;, score=0.797 total time= 0.0s
[CV 1/5; 233/400] START max_depth=6, min_samples_leaf=4, n_estimators=3.........
[CV 1/5; 233/400] END max_depth=6, min_samples_leaf=4, n_estimators=3;, score=0.819 total time= 0.0s
[CV 2/5; 233/400] START max_depth=6, min_samples_leaf=4, n_estimators=3.........
[CV 2/5; 233/400] END max_depth=6, min_samples_leaf=4, n_estimators=3;, score=0.830 total time= 0.0s
[CV 3/5; 233/400] START max_depth=6, min_samples_leaf=4, n_estimators=3.........
[CV 3/5; 233/400] END max_depth=6, min_samples_leaf=4, n_estimators=3;, score=0.806 total time= 0.0s
[CV 4/5; 233/400] START max_depth=6, min_samples_leaf=4, n_estimators=3.........
[CV 4/5; 233/400] END max_depth=6, min_samples_leaf=4, n_estimators=3;, score=0.823 total time= 0.0s
[CV 5/5; 233/400] START max_depth=6, min_samples_leaf=4, n_estimators=3.........
[CV 5/5; 233/400] END max_depth=6, min_samples_leaf=4, n_estimators=3;, score=0.800 total time= 0.0s
[CV 1/5; 234/400] START max_depth=6, min_samples_leaf=4, n_estimators=4.........
[CV 1/5; 234/400] END max_depth=6, min_samples_leaf=4, n_estimators=4;, score=0.805 total time= 0.0s
[CV 2/5; 234/400] START max_depth=6, min_samples_leaf=4, n_estimators=4.........
[CV 2/5; 234/400] END max_depth=6, min_samples_leaf=4, n_estimators=4;, score=0.810 total time= 0.0s
[CV 3/5; 234/400] START max_depth=6, min_samples_leaf=4, n_estimators=4.........
[CV 3/5; 234/400] END max_depth=6, min_samples_leaf=4, n_estimators=4;, score=0.810 total time= 0.0s
[CV 4/5; 234/400] START max_depth=6, min_samples_leaf=4, n_estimators=4.........
[CV 4/5; 234/400] END max_depth=6, min_samples_leaf=4, n_estimators=4;, score=0.808 total time= 0.0s
[CV 5/5; 234/400] START max_depth=6, min_samples_leaf=4, n_estimators=4.........
[CV 5/5; 234/400] END max_depth=6, min_samples_leaf=4, n_estimators=4;, score=0.808 total time= 0.0s
[CV 1/5; 235/400] START max_depth=6, min_samples_leaf=4, n_estimators=5.........
[CV 1/5; 235/400] END max_depth=6, min_samples_leaf=4, n_estimators=5;, score=0.816 total time= 0.0s
[CV 2/5; 235/400] START max_depth=6, min_samples_leaf=4, n_estimators=5.........
[CV 2/5; 235/400] END max_depth=6, min_samples_leaf=4, n_estimators=5;, score=0.819 total time= 0.0s
[CV 3/5; 235/400] START max_depth=6, min_samples_leaf=4, n_estimators=5.........
[CV 3/5; 235/400] END max_depth=6, min_samples_leaf=4, n_estimators=5;, score=0.817 total time= 0.0s
[CV 4/5; 235/400] START max_depth=6, min_samples_leaf=4, n_estimators=5.........
[CV 4/5; 235/400] END max_depth=6, min_samples_leaf=4, n_estimators=5;, score=0.822 total time= 0.0s
[CV 5/5; 235/400] START max_depth=6, min_samples_leaf=4, n_estimators=5.........
[CV 5/5; 235/400] END max_depth=6, min_samples_leaf=4, n_estimators=5;, score=0.799 total time= 0.0s
[CV 1/5; 236/400] START max_depth=6, min_samples_leaf=4, n_estimators=6.........
[CV 1/5; 236/400] END max_depth=6, min_samples_leaf=4, n_estimators=6;, score=0.817 total time= 0.0s
[CV 2/5; 236/400] START max_depth=6, min_samples_leaf=4, n_estimators=6.........
[CV 2/5; 236/400] END max_depth=6, min_samples_leaf=4, n_estimators=6;, score=0.820 total time= 0.0s
[CV 3/5; 236/400] START max_depth=6, min_samples_leaf=4, n_estimators=6.........
[CV 3/5; 236/400] END max_depth=6, min_samples_leaf=4, n_estimators=6;, score=0.806 total time= 0.0s
[CV 4/5; 236/400] START max_depth=6, min_samples_leaf=4, n_estimators=6.........
[CV 4/5; 236/400] END max_depth=6, min_samples_leaf=4, n_estimators=6;, score=0.812 total time= 0.0s
[CV 5/5; 236/400] START max_depth=6, min_samples_leaf=4, n_estimators=6.........
[CV 5/5; 236/400] END max_depth=6, min_samples_leaf=4, n_estimators=6;, score=0.805 total time= 0.0s
[CV 1/5; 237/400] START max_depth=6, min_samples_leaf=4, n_estimators=7.........
[CV 1/5; 237/400] END max_depth=6, min_samples_leaf=4, n_estimators=7;, score=0.829 total time= 0.0s
[CV 2/5; 237/400] START max_depth=6, min_samples_leaf=4, n_estimators=7.........
[CV 2/5; 237/400] END max_depth=6, min_samples_leaf=4, n_estimators=7;, score=0.828 total time= 0.0s
[CV 3/5; 237/400] START max_depth=6, min_samples_leaf=4, n_estimators=7.........
[CV 3/5; 237/400] END max_depth=6, min_samples_leaf=4, n_estimators=7;, score=0.807 total time= 0.0s
[CV 4/5; 237/400] START max_depth=6, min_samples_leaf=4, n_estimators=7.........
[CV 4/5; 237/400] END max_depth=6, min_samples_leaf=4, n_estimators=7;, score=0.810 total time= 0.0s
[CV 5/5; 237/400] START max_depth=6, min_samples_leaf=4, n_estimators=7.........
[CV 5/5; 237/400] END max_depth=6, min_samples_leaf=4, n_estimators=7;, score=0.810 total time= 0.0s
[CV 1/5; 238/400] START max_depth=6, min_samples_leaf=4, n_estimators=8.........
[CV 1/5; 238/400] END max_depth=6, min_samples_leaf=4, n_estimators=8;, score=0.822 total time= 0.1s
[CV 2/5; 238/400] START max_depth=6, min_samples_leaf=4, n_estimators=8.........
[CV 2/5; 238/400] END max_depth=6, min_samples_leaf=4, n_estimators=8;, score=0.835 total time= 0.1s
[CV 3/5; 238/400] START max_depth=6, min_samples_leaf=4, n_estimators=8.........
[CV 3/5; 238/400] END max_depth=6, min_samples_leaf=4, n_estimators=8;, score=0.821 total time= 0.0s
[CV 4/5; 238/400] START max_depth=6, min_samples_leaf=4, n_estimators=8.........
[CV 4/5; 238/400] END max_depth=6, min_samples_leaf=4, n_estimators=8;, score=0.812 total time= 0.0s
[CV 5/5; 238/400] START max_depth=6, min_samples_leaf=4, n_estimators=8.........
[CV 5/5; 238/400] END max_depth=6, min_samples_leaf=4, n_estimators=8;, score=0.802 total time= 0.0s
[CV 1/5; 239/400] START max_depth=6, min_samples_leaf=4, n_estimators=9.........
[CV 1/5; 239/400] END max_depth=6, min_samples_leaf=4, n_estimators=9;, score=0.817 total time= 0.1s
[CV 2/5; 239/400] START max_depth=6, min_samples_leaf=4, n_estimators=9.........
[CV 2/5; 239/400] END max_depth=6, min_samples_leaf=4, n_estimators=9;, score=0.824 total time= 0.1s
[CV 3/5; 239/400] START max_depth=6, min_samples_leaf=4, n_estimators=9.........
[CV 3/5; 239/400] END max_depth=6, min_samples_leaf=4, n_estimators=9;, score=0.805 total time= 0.1s
[CV 4/5; 239/400] START max_depth=6, min_samples_leaf=4, n_estimators=9.........
[CV 4/5; 239/400] END max_depth=6, min_samples_leaf=4, n_estimators=9;, score=0.816 total time= 0.1s
[CV 5/5; 239/400] START max_depth=6, min_samples_leaf=4, n_estimators=9.........
[CV 5/5; 239/400] END max_depth=6, min_samples_leaf=4, n_estimators=9;, score=0.805 total time= 0.1s
[CV 1/5; 240/400] START max_depth=6, min_samples_leaf=4, n_estimators=10........
[CV 1/5; 240/400] END max_depth=6, min_samples_leaf=4, n_estimators=10;, score=0.818 total time= 0.1s
[CV 2/5; 240/400] START max_depth=6, min_samples_leaf=4, n_estimators=10........
[CV 2/5; 240/400] END max_depth=6, min_samples_leaf=4, n_estimators=10;, score=0.819 total time= 0.1s
[CV 3/5; 240/400] START max_depth=6, min_samples_leaf=4, n_estimators=10........
[CV 3/5; 240/400] END max_depth=6, min_samples_leaf=4, n_estimators=10;, score=0.807 total time= 0.1s
[CV 4/5; 240/400] START max_depth=6, min_samples_leaf=4, n_estimators=10........
[CV 4/5; 240/400] END max_depth=6, min_samples_leaf=4, n_estimators=10;, score=0.807 total time= 0.1s
[CV 5/5; 240/400] START max_depth=6, min_samples_leaf=4, n_estimators=10........
[CV 5/5; 240/400] END max_depth=6, min_samples_leaf=4, n_estimators=10;, score=0.810 total time= 0.1s
[CV 1/5; 241/400] START max_depth=7, min_samples_leaf=1, n_estimators=1.........
[CV 1/5; 241/400] END max_depth=7, min_samples_leaf=1, n_estimators=1;, score=0.784 total time= 0.0s
[CV 2/5; 241/400] START max_depth=7, min_samples_leaf=1, n_estimators=1.........
[CV 2/5; 241/400] END max_depth=7, min_samples_leaf=1, n_estimators=1;, score=0.817 total time= 0.0s
[CV 3/5; 241/400] START max_depth=7, min_samples_leaf=1, n_estimators=1.........
[CV 3/5; 241/400] END max_depth=7, min_samples_leaf=1, n_estimators=1;, score=0.752 total time= 0.0s
[CV 4/5; 241/400] START max_depth=7, min_samples_leaf=1, n_estimators=1.........
[CV 4/5; 241/400] END max_depth=7, min_samples_leaf=1, n_estimators=1;, score=0.804 total time= 0.0s
[CV 5/5; 241/400] START max_depth=7, min_samples_leaf=1, n_estimators=1.........
[CV 5/5; 241/400] END max_depth=7, min_samples_leaf=1, n_estimators=1;, score=0.796 total time= 0.0s
[CV 1/5; 242/400] START max_depth=7, min_samples_leaf=1, n_estimators=2.........
[CV 1/5; 242/400] END max_depth=7, min_samples_leaf=1, n_estimators=2;, score=0.821 total time= 0.0s
[CV 2/5; 242/400] START max_depth=7, min_samples_leaf=1, n_estimators=2.........
[CV 2/5; 242/400] END max_depth=7, min_samples_leaf=1, n_estimators=2;, score=0.825 total time= 0.0s
[CV 3/5; 242/400] START max_depth=7, min_samples_leaf=1, n_estimators=2.........
[CV 3/5; 242/400] END max_depth=7, min_samples_leaf=1, n_estimators=2;, score=0.809 total time= 0.0s
[CV 4/5; 242/400] START max_depth=7, min_samples_leaf=1, n_estimators=2.........
[CV 4/5; 242/400] END max_depth=7, min_samples_leaf=1, n_estimators=2;, score=0.826 total time= 0.0s
[CV 5/5; 242/400] START max_depth=7, min_samples_leaf=1, n_estimators=2.........
[CV 5/5; 242/400] END max_depth=7, min_samples_leaf=1, n_estimators=2;, score=0.799 total time= 0.0s
[CV 1/5; 243/400] START max_depth=7, min_samples_leaf=1, n_estimators=3.........
[CV 1/5; 243/400] END max_depth=7, min_samples_leaf=1, n_estimators=3;, score=0.821 total time= 0.0s
[CV 2/5; 243/400] START max_depth=7, min_samples_leaf=1, n_estimators=3.........
[CV 2/5; 243/400] END max_depth=7, min_samples_leaf=1, n_estimators=3;, score=0.814 total time= 0.0s
[CV 3/5; 243/400] START max_depth=7, min_samples_leaf=1, n_estimators=3.........
[CV 3/5; 243/400] END max_depth=7, min_samples_leaf=1, n_estimators=3;, score=0.837 total time= 0.0s
[CV 4/5; 243/400] START max_depth=7, min_samples_leaf=1, n_estimators=3.........
[CV 4/5; 243/400] END max_depth=7, min_samples_leaf=1, n_estimators=3;, score=0.832 total time= 0.0s
[CV 5/5; 243/400] START max_depth=7, min_samples_leaf=1, n_estimators=3.........
[CV 5/5; 243/400] END max_depth=7, min_samples_leaf=1, n_estimators=3;, score=0.803 total time= 0.0s
[CV 1/5; 244/400] START max_depth=7, min_samples_leaf=1, n_estimators=4.........
[CV 1/5; 244/400] END max_depth=7, min_samples_leaf=1, n_estimators=4;, score=0.828 total time= 0.0s
[CV 2/5; 244/400] START max_depth=7, min_samples_leaf=1, n_estimators=4.........
[CV 2/5; 244/400] END max_depth=7, min_samples_leaf=1, n_estimators=4;, score=0.825 total time= 0.0s
[CV 3/5; 244/400] START max_depth=7, min_samples_leaf=1, n_estimators=4.........
[CV 3/5; 244/400] END max_depth=7, min_samples_leaf=1, n_estimators=4;, score=0.816 total time= 0.0s
[CV 4/5; 244/400] START max_depth=7, min_samples_leaf=1, n_estimators=4.........
[CV 4/5; 244/400] END max_depth=7, min_samples_leaf=1, n_estimators=4;, score=0.820 total time= 0.0s
[CV 5/5; 244/400] START max_depth=7, min_samples_leaf=1, n_estimators=4.........
[CV 5/5; 244/400] END max_depth=7, min_samples_leaf=1, n_estimators=4;, score=0.820 total time= 0.0s
[CV 1/5; 245/400] START max_depth=7, min_samples_leaf=1, n_estimators=5.........
[CV 1/5; 245/400] END max_depth=7, min_samples_leaf=1, n_estimators=5;, score=0.828 total time= 0.0s
[CV 2/5; 245/400] START max_depth=7, min_samples_leaf=1, n_estimators=5.........
[CV 2/5; 245/400] END max_depth=7, min_samples_leaf=1, n_estimators=5;, score=0.842 total time= 0.0s
[CV 3/5; 245/400] START max_depth=7, min_samples_leaf=1, n_estimators=5.........
[CV 3/5; 245/400] END max_depth=7, min_samples_leaf=1, n_estimators=5;, score=0.824 total time= 0.0s
[CV 4/5; 245/400] START max_depth=7, min_samples_leaf=1, n_estimators=5.........
[CV 4/5; 245/400] END max_depth=7, min_samples_leaf=1, n_estimators=5;, score=0.838 total time= 0.0s
[CV 5/5; 245/400] START max_depth=7, min_samples_leaf=1, n_estimators=5.........
[CV 5/5; 245/400] END max_depth=7, min_samples_leaf=1, n_estimators=5;, score=0.829 total time= 0.0s
[CV 1/5; 246/400] START max_depth=7, min_samples_leaf=1, n_estimators=6.........
[CV 1/5; 246/400] END max_depth=7, min_samples_leaf=1, n_estimators=6;, score=0.836 total time= 0.1s
[CV 2/5; 246/400] START max_depth=7, min_samples_leaf=1, n_estimators=6.........
[CV 2/5; 246/400] END max_depth=7, min_samples_leaf=1, n_estimators=6;, score=0.843 total time= 0.1s
[CV 3/5; 246/400] START max_depth=7, min_samples_leaf=1, n_estimators=6.........
[CV 3/5; 246/400] END max_depth=7, min_samples_leaf=1, n_estimators=6;, score=0.829 total time= 0.1s
[CV 4/5; 246/400] START max_depth=7, min_samples_leaf=1, n_estimators=6.........
[CV 4/5; 246/400] END max_depth=7, min_samples_leaf=1, n_estimators=6;, score=0.819 total time= 0.1s
[CV 5/5; 246/400] START max_depth=7, min_samples_leaf=1, n_estimators=6.........
[CV 5/5; 246/400] END max_depth=7, min_samples_leaf=1, n_estimators=6;, score=0.828 total time= 0.1s
[CV 1/5; 247/400] START max_depth=7, min_samples_leaf=1, n_estimators=7.........
[CV 1/5; 247/400] END max_depth=7, min_samples_leaf=1, n_estimators=7;, score=0.832 total time= 0.1s
[CV 2/5; 247/400] START max_depth=7, min_samples_leaf=1, n_estimators=7.........
[CV 2/5; 247/400] END max_depth=7, min_samples_leaf=1, n_estimators=7;, score=0.835 total time= 0.1s
[CV 3/5; 247/400] START max_depth=7, min_samples_leaf=1, n_estimators=7.........
[CV 3/5; 247/400] END max_depth=7, min_samples_leaf=1, n_estimators=7;, score=0.828 total time= 0.1s
[CV 4/5; 247/400] START max_depth=7, min_samples_leaf=1, n_estimators=7.........
[CV 4/5; 247/400] END max_depth=7, min_samples_leaf=1, n_estimators=7;, score=0.840 total time= 0.1s
[CV 5/5; 247/400] START max_depth=7, min_samples_leaf=1, n_estimators=7.........
[CV 5/5; 247/400] END max_depth=7, min_samples_leaf=1, n_estimators=7;, score=0.816 total time= 0.1s
[CV 1/5; 248/400] START max_depth=7, min_samples_leaf=1, n_estimators=8.........
[CV 1/5; 248/400] END max_depth=7, min_samples_leaf=1, n_estimators=8;, score=0.838 total time= 0.1s
[CV 2/5; 248/400] START max_depth=7, min_samples_leaf=1, n_estimators=8.........
[CV 2/5; 248/400] END max_depth=7, min_samples_leaf=1, n_estimators=8;, score=0.837 total time= 0.1s
[CV 3/5; 248/400] START max_depth=7, min_samples_leaf=1, n_estimators=8.........
[CV 3/5; 248/400] END max_depth=7, min_samples_leaf=1, n_estimators=8;, score=0.822 total time= 0.1s
[CV 4/5; 248/400] START max_depth=7, min_samples_leaf=1, n_estimators=8.........
[CV 4/5; 248/400] END max_depth=7, min_samples_leaf=1, n_estimators=8;, score=0.836 total time= 0.1s
[CV 5/5; 248/400] START max_depth=7, min_samples_leaf=1, n_estimators=8.........
[CV 5/5; 248/400] END max_depth=7, min_samples_leaf=1, n_estimators=8;, score=0.832 total time= 0.1s
[CV 1/5; 249/400] START max_depth=7, min_samples_leaf=1, n_estimators=9.........
[CV 1/5; 249/400] END max_depth=7, min_samples_leaf=1, n_estimators=9;, score=0.830 total time= 0.1s
[CV 2/5; 249/400] START max_depth=7, min_samples_leaf=1, n_estimators=9.........
[CV 2/5; 249/400] END max_depth=7, min_samples_leaf=1, n_estimators=9;, score=0.824 total time= 0.1s
[CV 3/5; 249/400] START max_depth=7, min_samples_leaf=1, n_estimators=9.........
[CV 3/5; 249/400] END max_depth=7, min_samples_leaf=1, n_estimators=9;, score=0.823 total time= 0.1s
[CV 4/5; 249/400] START max_depth=7, min_samples_leaf=1, n_estimators=9.........
[CV 4/5; 249/400] END max_depth=7, min_samples_leaf=1, n_estimators=9;, score=0.834 total time= 0.1s
[CV 5/5; 249/400] START max_depth=7, min_samples_leaf=1, n_estimators=9.........
[CV 5/5; 249/400] END max_depth=7, min_samples_leaf=1, n_estimators=9;, score=0.830 total time= 0.1s
[CV 1/5; 250/400] START max_depth=7, min_samples_leaf=1, n_estimators=10........
[CV 1/5; 250/400] END max_depth=7, min_samples_leaf=1, n_estimators=10;, score=0.830 total time= 0.1s
[CV 2/5; 250/400] START max_depth=7, min_samples_leaf=1, n_estimators=10........
[CV 2/5; 250/400] END max_depth=7, min_samples_leaf=1, n_estimators=10;, score=0.826 total time= 0.1s
[CV 3/5; 250/400] START max_depth=7, min_samples_leaf=1, n_estimators=10........
[CV 3/5; 250/400] END max_depth=7, min_samples_leaf=1, n_estimators=10;, score=0.829 total time= 0.1s
[CV 4/5; 250/400] START max_depth=7, min_samples_leaf=1, n_estimators=10........
[CV 4/5; 250/400] END max_depth=7, min_samples_leaf=1, n_estimators=10;, score=0.840 total time= 0.1s
[CV 5/5; 250/400] START max_depth=7, min_samples_leaf=1, n_estimators=10........
[CV 5/5; 250/400] END max_depth=7, min_samples_leaf=1, n_estimators=10;, score=0.834 total time= 0.1s
[CV 1/5; 251/400] START max_depth=7, min_samples_leaf=2, n_estimators=1.........
[CV 1/5; 251/400] END max_depth=7, min_samples_leaf=2, n_estimators=1;, score=0.815 total time= 0.0s
[CV 2/5; 251/400] START max_depth=7, min_samples_leaf=2, n_estimators=1.........
[CV 2/5; 251/400] END max_depth=7, min_samples_leaf=2, n_estimators=1;, score=0.717 total time= 0.0s
[CV 3/5; 251/400] START max_depth=7, min_samples_leaf=2, n_estimators=1.........
[CV 3/5; 251/400] END max_depth=7, min_samples_leaf=2, n_estimators=1;, score=0.801 total time= 0.0s
[CV 4/5; 251/400] START max_depth=7, min_samples_leaf=2, n_estimators=1.........
[CV 4/5; 251/400] END max_depth=7, min_samples_leaf=2, n_estimators=1;, score=0.791 total time= 0.0s
[CV 5/5; 251/400] START max_depth=7, min_samples_leaf=2, n_estimators=1.........
[CV 5/5; 251/400] END max_depth=7, min_samples_leaf=2, n_estimators=1;, score=0.788 total time= 0.0s
[CV 1/5; 252/400] START max_depth=7, min_samples_leaf=2, n_estimators=2.........
[CV 1/5; 252/400] END max_depth=7, min_samples_leaf=2, n_estimators=2;, score=0.821 total time= 0.0s
[CV 2/5; 252/400] START max_depth=7, min_samples_leaf=2, n_estimators=2.........
[CV 2/5; 252/400] END max_depth=7, min_samples_leaf=2, n_estimators=2;, score=0.812 total time= 0.0s
[CV 3/5; 252/400] START max_depth=7, min_samples_leaf=2, n_estimators=2.........
[CV 3/5; 252/400] END max_depth=7, min_samples_leaf=2, n_estimators=2;, score=0.796 total time= 0.0s
[CV 4/5; 252/400] START max_depth=7, min_samples_leaf=2, n_estimators=2.........
[CV 4/5; 252/400] END max_depth=7, min_samples_leaf=2, n_estimators=2;, score=0.832 total time= 0.0s
[CV 5/5; 252/400] START max_depth=7, min_samples_leaf=2, n_estimators=2.........
[CV 5/5; 252/400] END max_depth=7, min_samples_leaf=2, n_estimators=2;, score=0.790 total time= 0.0s
[CV 1/5; 253/400] START max_depth=7, min_samples_leaf=2, n_estimators=3.........
[CV 1/5; 253/400] END max_depth=7, min_samples_leaf=2, n_estimators=3;, score=0.830 total time= 0.0s
[CV 2/5; 253/400] START max_depth=7, min_samples_leaf=2, n_estimators=3.........
[CV 2/5; 253/400] END max_depth=7, min_samples_leaf=2, n_estimators=3;, score=0.821 total time= 0.0s
[CV 3/5; 253/400] START max_depth=7, min_samples_leaf=2, n_estimators=3.........
[CV 3/5; 253/400] END max_depth=7, min_samples_leaf=2, n_estimators=3;, score=0.814 total time= 0.0s
[CV 4/5; 253/400] START max_depth=7, min_samples_leaf=2, n_estimators=3.........
[CV 4/5; 253/400] END max_depth=7, min_samples_leaf=2, n_estimators=3;, score=0.806 total time= 0.0s
[CV 5/5; 253/400] START max_depth=7, min_samples_leaf=2, n_estimators=3.........
[CV 5/5; 253/400] END max_depth=7, min_samples_leaf=2, n_estimators=3;, score=0.814 total time= 0.0s
[CV 1/5; 254/400] START max_depth=7, min_samples_leaf=2, n_estimators=4.........
[CV 1/5; 254/400] END max_depth=7, min_samples_leaf=2, n_estimators=4;, score=0.825 total time= 0.0s
[CV 2/5; 254/400] START max_depth=7, min_samples_leaf=2, n_estimators=4.........
[CV 2/5; 254/400] END max_depth=7, min_samples_leaf=2, n_estimators=4;, score=0.818 total time= 0.0s
[CV 3/5; 254/400] START max_depth=7, min_samples_leaf=2, n_estimators=4.........
[CV 3/5; 254/400] END max_depth=7, min_samples_leaf=2, n_estimators=4;, score=0.826 total time= 0.0s
[CV 4/5; 254/400] START max_depth=7, min_samples_leaf=2, n_estimators=4.........
[CV 4/5; 254/400] END max_depth=7, min_samples_leaf=2, n_estimators=4;, score=0.824 total time= 0.0s
[CV 5/5; 254/400] START max_depth=7, min_samples_leaf=2, n_estimators=4.........
[CV 5/5; 254/400] END max_depth=7, min_samples_leaf=2, n_estimators=4;, score=0.809 total time= 0.0s
[CV 1/5; 255/400] START max_depth=7, min_samples_leaf=2, n_estimators=5.........
[CV 1/5; 255/400] END max_depth=7, min_samples_leaf=2, n_estimators=5;, score=0.829 total time= 0.0s
[CV 2/5; 255/400] START max_depth=7, min_samples_leaf=2, n_estimators=5.........
[CV 2/5; 255/400] END max_depth=7, min_samples_leaf=2, n_estimators=5;, score=0.834 total time= 0.0s
[CV 3/5; 255/400] START max_depth=7, min_samples_leaf=2, n_estimators=5.........
[CV 3/5; 255/400] END max_depth=7, min_samples_leaf=2, n_estimators=5;, score=0.823 total time= 0.0s
[CV 4/5; 255/400] START max_depth=7, min_samples_leaf=2, n_estimators=5.........
[CV 4/5; 255/400] END max_depth=7, min_samples_leaf=2, n_estimators=5;, score=0.834 total time= 0.0s
[CV 5/5; 255/400] START max_depth=7, min_samples_leaf=2, n_estimators=5.........
[CV 5/5; 255/400] END max_depth=7, min_samples_leaf=2, n_estimators=5;, score=0.812 total time= 0.0s
[CV 1/5; 256/400] START max_depth=7, min_samples_leaf=2, n_estimators=6.........
[CV 1/5; 256/400] END max_depth=7, min_samples_leaf=2, n_estimators=6;, score=0.832 total time= 0.0s
[CV 2/5; 256/400] START max_depth=7, min_samples_leaf=2, n_estimators=6.........
[CV 2/5; 256/400] END max_depth=7, min_samples_leaf=2, n_estimators=6;, score=0.838 total time= 0.0s
[CV 3/5; 256/400] START max_depth=7, min_samples_leaf=2, n_estimators=6.........
[CV 3/5; 256/400] END max_depth=7, min_samples_leaf=2, n_estimators=6;, score=0.823 total time= 0.0s
[CV 4/5; 256/400] START max_depth=7, min_samples_leaf=2, n_estimators=6.........
[CV 4/5; 256/400] END max_depth=7, min_samples_leaf=2, n_estimators=6;, score=0.837 total time= 0.0s
[CV 5/5; 256/400] START max_depth=7, min_samples_leaf=2, n_estimators=6.........
[CV 5/5; 256/400] END max_depth=7, min_samples_leaf=2, n_estimators=6;, score=0.813 total time= 0.0s
[CV 1/5; 257/400] START max_depth=7, min_samples_leaf=2, n_estimators=7.........
[CV 1/5; 257/400] END max_depth=7, min_samples_leaf=2, n_estimators=7;, score=0.828 total time= 0.1s
[CV 2/5; 257/400] START max_depth=7, min_samples_leaf=2, n_estimators=7.........
[CV 2/5; 257/400] END max_depth=7, min_samples_leaf=2, n_estimators=7;, score=0.827 total time= 0.0s
[CV 3/5; 257/400] START max_depth=7, min_samples_leaf=2, n_estimators=7.........
[CV 3/5; 257/400] END max_depth=7, min_samples_leaf=2, n_estimators=7;, score=0.830 total time= 0.1s
[CV 4/5; 257/400] START max_depth=7, min_samples_leaf=2, n_estimators=7.........
[CV 4/5; 257/400] END max_depth=7, min_samples_leaf=2, n_estimators=7;, score=0.838 total time= 0.0s
[CV 5/5; 257/400] START max_depth=7, min_samples_leaf=2, n_estimators=7.........
[CV 5/5; 257/400] END max_depth=7, min_samples_leaf=2, n_estimators=7;, score=0.827 total time= 0.0s
[CV 1/5; 258/400] START max_depth=7, min_samples_leaf=2, n_estimators=8.........
[CV 1/5; 258/400] END max_depth=7, min_samples_leaf=2, n_estimators=8;, score=0.832 total time= 0.1s
[CV 2/5; 258/400] START max_depth=7, min_samples_leaf=2, n_estimators=8.........
[CV 2/5; 258/400] END max_depth=7, min_samples_leaf=2, n_estimators=8;, score=0.828 total time= 0.1s
[CV 3/5; 258/400] START max_depth=7, min_samples_leaf=2, n_estimators=8.........
[CV 3/5; 258/400] END max_depth=7, min_samples_leaf=2, n_estimators=8;, score=0.830 total time= 0.1s
[CV 4/5; 258/400] START max_depth=7, min_samples_leaf=2, n_estimators=8.........
[CV 4/5; 258/400] END max_depth=7, min_samples_leaf=2, n_estimators=8;, score=0.838 total time= 0.1s
[CV 5/5; 258/400] START max_depth=7, min_samples_leaf=2, n_estimators=8.........
[CV 5/5; 258/400] END max_depth=7, min_samples_leaf=2, n_estimators=8;, score=0.832 total time= 0.1s
[CV 1/5; 259/400] START max_depth=7, min_samples_leaf=2, n_estimators=9.........
[CV 1/5; 259/400] END max_depth=7, min_samples_leaf=2, n_estimators=9;, score=0.826 total time= 0.1s
[CV 2/5; 259/400] START max_depth=7, min_samples_leaf=2, n_estimators=9.........
[CV 2/5; 259/400] END max_depth=7, min_samples_leaf=2, n_estimators=9;, score=0.841 total time= 0.1s
[CV 3/5; 259/400] START max_depth=7, min_samples_leaf=2, n_estimators=9.........
[CV 3/5; 259/400] END max_depth=7, min_samples_leaf=2, n_estimators=9;, score=0.841 total time= 0.1s
[CV 4/5; 259/400] START max_depth=7, min_samples_leaf=2, n_estimators=9.........
[CV 4/5; 259/400] END max_depth=7, min_samples_leaf=2, n_estimators=9;, score=0.837 total time= 0.1s
[CV 5/5; 259/400] START max_depth=7, min_samples_leaf=2, n_estimators=9.........
[CV 5/5; 259/400] END max_depth=7, min_samples_leaf=2, n_estimators=9;, score=0.832 total time= 0.1s
[CV 1/5; 260/400] START max_depth=7, min_samples_leaf=2, n_estimators=10........
[CV 1/5; 260/400] END max_depth=7, min_samples_leaf=2, n_estimators=10;, score=0.835 total time= 0.1s
[CV 2/5; 260/400] START max_depth=7, min_samples_leaf=2, n_estimators=10........
[CV 2/5; 260/400] END max_depth=7, min_samples_leaf=2, n_estimators=10;, score=0.841 total time= 0.1s
[CV 3/5; 260/400] START max_depth=7, min_samples_leaf=2, n_estimators=10........
[CV 3/5; 260/400] END max_depth=7, min_samples_leaf=2, n_estimators=10;, score=0.827 total time= 0.1s
[CV 4/5; 260/400] START max_depth=7, min_samples_leaf=2, n_estimators=10........
[CV 4/5; 260/400] END max_depth=7, min_samples_leaf=2, n_estimators=10;, score=0.835 total time= 0.1s
[CV 5/5; 260/400] START max_depth=7, min_samples_leaf=2, n_estimators=10........
[CV 5/5; 260/400] END max_depth=7, min_samples_leaf=2, n_estimators=10;, score=0.827 total time= 0.1s
[CV 1/5; 261/400] START max_depth=7, min_samples_leaf=3, n_estimators=1.........
[CV 1/5; 261/400] END max_depth=7, min_samples_leaf=3, n_estimators=1;, score=0.815 total time= 0.0s
[CV 2/5; 261/400] START max_depth=7, min_samples_leaf=3, n_estimators=1.........
[CV 2/5; 261/400] END max_depth=7, min_samples_leaf=3, n_estimators=1;, score=0.820 total time= 0.0s
[CV 3/5; 261/400] START max_depth=7, min_samples_leaf=3, n_estimators=1.........
[CV 3/5; 261/400] END max_depth=7, min_samples_leaf=3, n_estimators=1;, score=0.782 total time= 0.0s
[CV 4/5; 261/400] START max_depth=7, min_samples_leaf=3, n_estimators=1.........
[CV 4/5; 261/400] END max_depth=7, min_samples_leaf=3, n_estimators=1;, score=0.814 total time= 0.0s
[CV 5/5; 261/400] START max_depth=7, min_samples_leaf=3, n_estimators=1.........
[CV 5/5; 261/400] END max_depth=7, min_samples_leaf=3, n_estimators=1;, score=0.798 total time= 0.0s
[CV 1/5; 262/400] START max_depth=7, min_samples_leaf=3, n_estimators=2.........
[CV 1/5; 262/400] END max_depth=7, min_samples_leaf=3, n_estimators=2;, score=0.822 total time= 0.0s
[CV 2/5; 262/400] START max_depth=7, min_samples_leaf=3, n_estimators=2.........
[CV 2/5; 262/400] END max_depth=7, min_samples_leaf=3, n_estimators=2;, score=0.820 total time= 0.0s
[CV 3/5; 262/400] START max_depth=7, min_samples_leaf=3, n_estimators=2.........
[CV 3/5; 262/400] END max_depth=7, min_samples_leaf=3, n_estimators=2;, score=0.783 total time= 0.0s
[CV 4/5; 262/400] START max_depth=7, min_samples_leaf=3, n_estimators=2.........
[CV 4/5; 262/400] END max_depth=7, min_samples_leaf=3, n_estimators=2;, score=0.833 total time= 0.0s
[CV 5/5; 262/400] START max_depth=7, min_samples_leaf=3, n_estimators=2.........
[CV 5/5; 262/400] END max_depth=7, min_samples_leaf=3, n_estimators=2;, score=0.819 total time= 0.0s
[CV 1/5; 263/400] START max_depth=7, min_samples_leaf=3, n_estimators=3.........
[CV 1/5; 263/400] END max_depth=7, min_samples_leaf=3, n_estimators=3;, score=0.821 total time= 0.0s
[CV 2/5; 263/400] START max_depth=7, min_samples_leaf=3, n_estimators=3.........
[CV 2/5; 263/400] END max_depth=7, min_samples_leaf=3, n_estimators=3;, score=0.816 total time= 0.0s
[CV 3/5; 263/400] START max_depth=7, min_samples_leaf=3, n_estimators=3.........
[CV 3/5; 263/400] END max_depth=7, min_samples_leaf=3, n_estimators=3;, score=0.820 total time= 0.0s
[CV 4/5; 263/400] START max_depth=7, min_samples_leaf=3, n_estimators=3.........
[CV 4/5; 263/400] END max_depth=7, min_samples_leaf=3, n_estimators=3;, score=0.821 total time= 0.0s
[CV 5/5; 263/400] START max_depth=7, min_samples_leaf=3, n_estimators=3.........
[CV 5/5; 263/400] END max_depth=7, min_samples_leaf=3, n_estimators=3;, score=0.828 total time= 0.0s
[CV 1/5; 264/400] START max_depth=7, min_samples_leaf=3, n_estimators=4.........
[CV 1/5; 264/400] END max_depth=7, min_samples_leaf=3, n_estimators=4;, score=0.838 total time= 0.0s
[CV 2/5; 264/400] START max_depth=7, min_samples_leaf=3, n_estimators=4.........
[CV 2/5; 264/400] END max_depth=7, min_samples_leaf=3, n_estimators=4;, score=0.850 total time= 0.0s
[CV 3/5; 264/400] START max_depth=7, min_samples_leaf=3, n_estimators=4.........
[CV 3/5; 264/400] END max_depth=7, min_samples_leaf=3, n_estimators=4;, score=0.830 total time= 0.0s
[CV 4/5; 264/400] START max_depth=7, min_samples_leaf=3, n_estimators=4.........
[CV 4/5; 264/400] END max_depth=7, min_samples_leaf=3, n_estimators=4;, score=0.813 total time= 0.0s
[CV 5/5; 264/400] START max_depth=7, min_samples_leaf=3, n_estimators=4.........
[CV 5/5; 264/400] END max_depth=7, min_samples_leaf=3, n_estimators=4;, score=0.819 total time= 0.0s
[CV 1/5; 265/400] START max_depth=7, min_samples_leaf=3, n_estimators=5.........
[CV 1/5; 265/400] END max_depth=7, min_samples_leaf=3, n_estimators=5;, score=0.846 total time= 0.0s
[CV 2/5; 265/400] START max_depth=7, min_samples_leaf=3, n_estimators=5.........
[CV 2/5; 265/400] END max_depth=7, min_samples_leaf=3, n_estimators=5;, score=0.821 total time= 0.0s
[CV 3/5; 265/400] START max_depth=7, min_samples_leaf=3, n_estimators=5.........
[CV 3/5; 265/400] END max_depth=7, min_samples_leaf=3, n_estimators=5;, score=0.821 total time= 0.0s
[CV 4/5; 265/400] START max_depth=7, min_samples_leaf=3, n_estimators=5.........
[CV 4/5; 265/400] END max_depth=7, min_samples_leaf=3, n_estimators=5;, score=0.834 total time= 0.0s
[CV 5/5; 265/400] START max_depth=7, min_samples_leaf=3, n_estimators=5.........
[CV 5/5; 265/400] END max_depth=7, min_samples_leaf=3, n_estimators=5;, score=0.813 total time= 0.0s
[CV 1/5; 266/400] START max_depth=7, min_samples_leaf=3, n_estimators=6.........
[CV 1/5; 266/400] END max_depth=7, min_samples_leaf=3, n_estimators=6;, score=0.834 total time= 0.0s
[CV 2/5; 266/400] START max_depth=7, min_samples_leaf=3, n_estimators=6.........
[CV 2/5; 266/400] END max_depth=7, min_samples_leaf=3, n_estimators=6;, score=0.823 total time= 0.0s
[CV 3/5; 266/400] START max_depth=7, min_samples_leaf=3, n_estimators=6.........
[CV 3/5; 266/400] END max_depth=7, min_samples_leaf=3, n_estimators=6;, score=0.825 total time= 0.0s
[CV 4/5; 266/400] START max_depth=7, min_samples_leaf=3, n_estimators=6.........
[CV 4/5; 266/400] END max_depth=7, min_samples_leaf=3, n_estimators=6;, score=0.835 total time= 0.0s
[CV 5/5; 266/400] START max_depth=7, min_samples_leaf=3, n_estimators=6.........
[CV 5/5; 266/400] END max_depth=7, min_samples_leaf=3, n_estimators=6;, score=0.813 total time= 0.0s
[CV 1/5; 267/400] START max_depth=7, min_samples_leaf=3, n_estimators=7.........
[CV 1/5; 267/400] END max_depth=7, min_samples_leaf=3, n_estimators=7;, score=0.831 total time= 0.0s
[CV 2/5; 267/400] START max_depth=7, min_samples_leaf=3, n_estimators=7.........
[CV 2/5; 267/400] END max_depth=7, min_samples_leaf=3, n_estimators=7;, score=0.833 total time= 0.0s
[CV 3/5; 267/400] START max_depth=7, min_samples_leaf=3, n_estimators=7.........
[CV 3/5; 267/400] END max_depth=7, min_samples_leaf=3, n_estimators=7;, score=0.821 total time= 0.1s
[CV 4/5; 267/400] START max_depth=7, min_samples_leaf=3, n_estimators=7.........
[CV 4/5; 267/400] END max_depth=7, min_samples_leaf=3, n_estimators=7;, score=0.827 total time= 0.0s
[CV 5/5; 267/400] START max_depth=7, min_samples_leaf=3, n_estimators=7.........
[CV 5/5; 267/400] END max_depth=7, min_samples_leaf=3, n_estimators=7;, score=0.826 total time= 0.0s
[CV 1/5; 268/400] START max_depth=7, min_samples_leaf=3, n_estimators=8.........
[CV 1/5; 268/400] END max_depth=7, min_samples_leaf=3, n_estimators=8;, score=0.852 total time= 0.1s
[CV 2/5; 268/400] START max_depth=7, min_samples_leaf=3, n_estimators=8.........
[CV 2/5; 268/400] END max_depth=7, min_samples_leaf=3, n_estimators=8;, score=0.838 total time= 0.0s
[CV 3/5; 268/400] START max_depth=7, min_samples_leaf=3, n_estimators=8.........
[CV 3/5; 268/400] END max_depth=7, min_samples_leaf=3, n_estimators=8;, score=0.821 total time= 0.1s
[CV 4/5; 268/400] START max_depth=7, min_samples_leaf=3, n_estimators=8.........
[CV 4/5; 268/400] END max_depth=7, min_samples_leaf=3, n_estimators=8;, score=0.830 total time= 0.0s
[CV 5/5; 268/400] START max_depth=7, min_samples_leaf=3, n_estimators=8.........
[CV 5/5; 268/400] END max_depth=7, min_samples_leaf=3, n_estimators=8;, score=0.820 total time= 0.1s
[CV 1/5; 269/400] START max_depth=7, min_samples_leaf=3, n_estimators=9.........
[CV 1/5; 269/400] END max_depth=7, min_samples_leaf=3, n_estimators=9;, score=0.832 total time= 0.1s
[CV 2/5; 269/400] START max_depth=7, min_samples_leaf=3, n_estimators=9.........
[CV 2/5; 269/400] END max_depth=7, min_samples_leaf=3, n_estimators=9;, score=0.815 total time= 0.1s
[CV 3/5; 269/400] START max_depth=7, min_samples_leaf=3, n_estimators=9.........
[CV 3/5; 269/400] END max_depth=7, min_samples_leaf=3, n_estimators=9;, score=0.821 total time= 0.1s
[CV 4/5; 269/400] START max_depth=7, min_samples_leaf=3, n_estimators=9.........
[CV 4/5; 269/400] END max_depth=7, min_samples_leaf=3, n_estimators=9;, score=0.836 total time= 0.1s
[CV 5/5; 269/400] START max_depth=7, min_samples_leaf=3, n_estimators=9.........
[CV 5/5; 269/400] END max_depth=7, min_samples_leaf=3, n_estimators=9;, score=0.819 total time= 0.1s
[CV 1/5; 270/400] START max_depth=7, min_samples_leaf=3, n_estimators=10........
[CV 1/5; 270/400] END max_depth=7, min_samples_leaf=3, n_estimators=10;, score=0.830 total time= 0.1s
[CV 2/5; 270/400] START max_depth=7, min_samples_leaf=3, n_estimators=10........
[CV 2/5; 270/400] END max_depth=7, min_samples_leaf=3, n_estimators=10;, score=0.835 total time= 0.1s
[CV 3/5; 270/400] START max_depth=7, min_samples_leaf=3, n_estimators=10........
[CV 3/5; 270/400] END max_depth=7, min_samples_leaf=3, n_estimators=10;, score=0.827 total time= 0.1s
[CV 4/5; 270/400] START max_depth=7, min_samples_leaf=3, n_estimators=10........
[CV 4/5; 270/400] END max_depth=7, min_samples_leaf=3, n_estimators=10;, score=0.830 total time= 0.1s
[CV 5/5; 270/400] START max_depth=7, min_samples_leaf=3, n_estimators=10........
[CV 5/5; 270/400] END max_depth=7, min_samples_leaf=3, n_estimators=10;, score=0.827 total time= 0.1s
[CV 1/5; 271/400] START max_depth=7, min_samples_leaf=4, n_estimators=1.........
[CV 1/5; 271/400] END max_depth=7, min_samples_leaf=4, n_estimators=1;, score=0.796 total time= 0.0s
[CV 2/5; 271/400] START max_depth=7, min_samples_leaf=4, n_estimators=1.........
[CV 2/5; 271/400] END max_depth=7, min_samples_leaf=4, n_estimators=1;, score=0.816 total time= 0.0s
[CV 3/5; 271/400] START max_depth=7, min_samples_leaf=4, n_estimators=1.........
[CV 3/5; 271/400] END max_depth=7, min_samples_leaf=4, n_estimators=1;, score=0.800 total time= 0.0s
[CV 4/5; 271/400] START max_depth=7, min_samples_leaf=4, n_estimators=1.........
[CV 4/5; 271/400] END max_depth=7, min_samples_leaf=4, n_estimators=1;, score=0.798 total time= 0.0s
[CV 5/5; 271/400] START max_depth=7, min_samples_leaf=4, n_estimators=1.........
[CV 5/5; 271/400] END max_depth=7, min_samples_leaf=4, n_estimators=1;, score=0.755 total time= 0.0s
[CV 1/5; 272/400] START max_depth=7, min_samples_leaf=4, n_estimators=2.........
[CV 1/5; 272/400] END max_depth=7, min_samples_leaf=4, n_estimators=2;, score=0.831 total time= 0.0s
[CV 2/5; 272/400] START max_depth=7, min_samples_leaf=4, n_estimators=2.........
[CV 2/5; 272/400] END max_depth=7, min_samples_leaf=4, n_estimators=2;, score=0.816 total time= 0.0s
[CV 3/5; 272/400] START max_depth=7, min_samples_leaf=4, n_estimators=2.........
[CV 3/5; 272/400] END max_depth=7, min_samples_leaf=4, n_estimators=2;, score=0.829 total time= 0.0s
[CV 4/5; 272/400] START max_depth=7, min_samples_leaf=4, n_estimators=2.........
[CV 4/5; 272/400] END max_depth=7, min_samples_leaf=4, n_estimators=2;, score=0.816 total time= 0.0s
[CV 5/5; 272/400] START max_depth=7, min_samples_leaf=4, n_estimators=2.........
[CV 5/5; 272/400] END max_depth=7, min_samples_leaf=4, n_estimators=2;, score=0.794 total time= 0.0s
[CV 1/5; 273/400] START max_depth=7, min_samples_leaf=4, n_estimators=3.........
[CV 1/5; 273/400] END max_depth=7, min_samples_leaf=4, n_estimators=3;, score=0.825 total time= 0.0s
[CV 2/5; 273/400] START max_depth=7, min_samples_leaf=4, n_estimators=3.........
[CV 2/5; 273/400] END max_depth=7, min_samples_leaf=4, n_estimators=3;, score=0.819 total time= 0.0s
[CV 3/5; 273/400] START max_depth=7, min_samples_leaf=4, n_estimators=3.........
[CV 3/5; 273/400] END max_depth=7, min_samples_leaf=4, n_estimators=3;, score=0.839 total time= 0.0s
[CV 4/5; 273/400] START max_depth=7, min_samples_leaf=4, n_estimators=3.........
[CV 4/5; 273/400] END max_depth=7, min_samples_leaf=4, n_estimators=3;, score=0.810 total time= 0.0s
[CV 5/5; 273/400] START max_depth=7, min_samples_leaf=4, n_estimators=3.........
[CV 5/5; 273/400] END max_depth=7, min_samples_leaf=4, n_estimators=3;, score=0.819 total time= 0.0s
[CV 1/5; 274/400] START max_depth=7, min_samples_leaf=4, n_estimators=4.........
[CV 1/5; 274/400] END max_depth=7, min_samples_leaf=4, n_estimators=4;, score=0.824 total time= 0.0s
[CV 2/5; 274/400] START max_depth=7, min_samples_leaf=4, n_estimators=4.........
[CV 2/5; 274/400] END max_depth=7, min_samples_leaf=4, n_estimators=4;, score=0.819 total time= 0.0s
[CV 3/5; 274/400] START max_depth=7, min_samples_leaf=4, n_estimators=4.........
[CV 3/5; 274/400] END max_depth=7, min_samples_leaf=4, n_estimators=4;, score=0.816 total time= 0.0s
[CV 4/5; 274/400] START max_depth=7, min_samples_leaf=4, n_estimators=4.........
[CV 4/5; 274/400] END max_depth=7, min_samples_leaf=4, n_estimators=4;, score=0.820 total time= 0.0s
[CV 5/5; 274/400] START max_depth=7, min_samples_leaf=4, n_estimators=4.........
[CV 5/5; 274/400] END max_depth=7, min_samples_leaf=4, n_estimators=4;, score=0.810 total time= 0.0s
[CV 1/5; 275/400] START max_depth=7, min_samples_leaf=4, n_estimators=5.........
[CV 1/5; 275/400] END max_depth=7, min_samples_leaf=4, n_estimators=5;, score=0.836 total time= 0.0s
[CV 2/5; 275/400] START max_depth=7, min_samples_leaf=4, n_estimators=5.........
[CV 2/5; 275/400] END max_depth=7, min_samples_leaf=4, n_estimators=5;, score=0.816 total time= 0.0s
[CV 3/5; 275/400] START max_depth=7, min_samples_leaf=4, n_estimators=5.........
[CV 3/5; 275/400] END max_depth=7, min_samples_leaf=4, n_estimators=5;, score=0.809 total time= 0.0s
[CV 4/5; 275/400] START max_depth=7, min_samples_leaf=4, n_estimators=5.........
[CV 4/5; 275/400] END max_depth=7, min_samples_leaf=4, n_estimators=5;, score=0.831 total time= 0.0s
[CV 5/5; 275/400] START max_depth=7, min_samples_leaf=4, n_estimators=5.........
[CV 5/5; 275/400] END max_depth=7, min_samples_leaf=4, n_estimators=5;, score=0.816 total time= 0.0s
[CV 1/5; 276/400] START max_depth=7, min_samples_leaf=4, n_estimators=6.........
[CV 1/5; 276/400] END max_depth=7, min_samples_leaf=4, n_estimators=6;, score=0.828 total time= 0.0s
[CV 2/5; 276/400] START max_depth=7, min_samples_leaf=4, n_estimators=6.........
[CV 2/5; 276/400] END max_depth=7, min_samples_leaf=4, n_estimators=6;, score=0.813 total time= 0.0s
[CV 3/5; 276/400] START max_depth=7, min_samples_leaf=4, n_estimators=6.........
[CV 3/5; 276/400] END max_depth=7, min_samples_leaf=4, n_estimators=6;, score=0.813 total time= 0.0s
[CV 4/5; 276/400] START max_depth=7, min_samples_leaf=4, n_estimators=6.........
[CV 4/5; 276/400] END max_depth=7, min_samples_leaf=4, n_estimators=6;, score=0.831 total time= 0.1s
[CV 5/5; 276/400] START max_depth=7, min_samples_leaf=4, n_estimators=6.........
[CV 5/5; 276/400] END max_depth=7, min_samples_leaf=4, n_estimators=6;, score=0.825 total time= 0.0s
[CV 1/5; 277/400] START max_depth=7, min_samples_leaf=4, n_estimators=7.........
[CV 1/5; 277/400] END max_depth=7, min_samples_leaf=4, n_estimators=7;, score=0.834 total time= 0.0s
[CV 2/5; 277/400] START max_depth=7, min_samples_leaf=4, n_estimators=7.........
[CV 2/5; 277/400] END max_depth=7, min_samples_leaf=4, n_estimators=7;, score=0.836 total time= 0.0s
[CV 3/5; 277/400] START max_depth=7, min_samples_leaf=4, n_estimators=7.........
[CV 3/5; 277/400] END max_depth=7, min_samples_leaf=4, n_estimators=7;, score=0.829 total time= 0.0s
[CV 4/5; 277/400] START max_depth=7, min_samples_leaf=4, n_estimators=7.........
[CV 4/5; 277/400] END max_depth=7, min_samples_leaf=4, n_estimators=7;, score=0.833 total time= 0.0s
[CV 5/5; 277/400] START max_depth=7, min_samples_leaf=4, n_estimators=7.........
[CV 5/5; 277/400] END max_depth=7, min_samples_leaf=4, n_estimators=7;, score=0.830 total time= 0.0s
[CV 1/5; 278/400] START max_depth=7, min_samples_leaf=4, n_estimators=8.........
[CV 1/5; 278/400] END max_depth=7, min_samples_leaf=4, n_estimators=8;, score=0.830 total time= 0.1s
[CV 2/5; 278/400] START max_depth=7, min_samples_leaf=4, n_estimators=8.........
[CV 2/5; 278/400] END max_depth=7, min_samples_leaf=4, n_estimators=8;, score=0.826 total time= 0.1s
[CV 3/5; 278/400] START max_depth=7, min_samples_leaf=4, n_estimators=8.........
[CV 3/5; 278/400] END max_depth=7, min_samples_leaf=4, n_estimators=8;, score=0.820 total time= 0.0s
[CV 4/5; 278/400] START max_depth=7, min_samples_leaf=4, n_estimators=8.........
[CV 4/5; 278/400] END max_depth=7, min_samples_leaf=4, n_estimators=8;, score=0.824 total time= 0.0s
[CV 5/5; 278/400] START max_depth=7, min_samples_leaf=4, n_estimators=8.........
[CV 5/5; 278/400] END max_depth=7, min_samples_leaf=4, n_estimators=8;, score=0.827 total time= 0.1s
[CV 1/5; 279/400] START max_depth=7, min_samples_leaf=4, n_estimators=9.........
[CV 1/5; 279/400] END max_depth=7, min_samples_leaf=4, n_estimators=9;, score=0.826 total time= 0.1s
[CV 2/5; 279/400] START max_depth=7, min_samples_leaf=4, n_estimators=9.........
[CV 2/5; 279/400] END max_depth=7, min_samples_leaf=4, n_estimators=9;, score=0.819 total time= 0.1s
[CV 3/5; 279/400] START max_depth=7, min_samples_leaf=4, n_estimators=9.........
[CV 3/5; 279/400] END max_depth=7, min_samples_leaf=4, n_estimators=9;, score=0.823 total time= 0.1s
[CV 4/5; 279/400] START max_depth=7, min_samples_leaf=4, n_estimators=9.........
[CV 4/5; 279/400] END max_depth=7, min_samples_leaf=4, n_estimators=9;, score=0.833 total time= 0.1s
[CV 5/5; 279/400] START max_depth=7, min_samples_leaf=4, n_estimators=9.........
[CV 5/5; 279/400] END max_depth=7, min_samples_leaf=4, n_estimators=9;, score=0.822 total time= 0.1s
[CV 1/5; 280/400] START max_depth=7, min_samples_leaf=4, n_estimators=10........
[CV 1/5; 280/400] END max_depth=7, min_samples_leaf=4, n_estimators=10;, score=0.832 total time= 0.1s
[CV 2/5; 280/400] START max_depth=7, min_samples_leaf=4, n_estimators=10........
[CV 2/5; 280/400] END max_depth=7, min_samples_leaf=4, n_estimators=10;, score=0.828 total time= 0.1s
[CV 3/5; 280/400] START max_depth=7, min_samples_leaf=4, n_estimators=10........
[CV 3/5; 280/400] END max_depth=7, min_samples_leaf=4, n_estimators=10;, score=0.835 total time= 0.1s
[CV 4/5; 280/400] START max_depth=7, min_samples_leaf=4, n_estimators=10........
[CV 4/5; 280/400] END max_depth=7, min_samples_leaf=4, n_estimators=10;, score=0.827 total time= 0.1s
[CV 5/5; 280/400] START max_depth=7, min_samples_leaf=4, n_estimators=10........
[CV 5/5; 280/400] END max_depth=7, min_samples_leaf=4, n_estimators=10;, score=0.812 total time= 0.1s
[CV 1/5; 281/400] START max_depth=8, min_samples_leaf=1, n_estimators=1.........
[CV 1/5; 281/400] END max_depth=8, min_samples_leaf=1, n_estimators=1;, score=0.805 total time= 0.0s
[CV 2/5; 281/400] START max_depth=8, min_samples_leaf=1, n_estimators=1.........
[CV 2/5; 281/400] END max_depth=8, min_samples_leaf=1, n_estimators=1;, score=0.819 total time= 0.0s
[CV 3/5; 281/400] START max_depth=8, min_samples_leaf=1, n_estimators=1.........
[CV 3/5; 281/400] END max_depth=8, min_samples_leaf=1, n_estimators=1;, score=0.754 total time= 0.0s
[CV 4/5; 281/400] START max_depth=8, min_samples_leaf=1, n_estimators=1.........
[CV 4/5; 281/400] END max_depth=8, min_samples_leaf=1, n_estimators=1;, score=0.808 total time= 0.0s
[CV 5/5; 281/400] START max_depth=8, min_samples_leaf=1, n_estimators=1.........
[CV 5/5; 281/400] END max_depth=8, min_samples_leaf=1, n_estimators=1;, score=0.802 total time= 0.0s
[CV 1/5; 282/400] START max_depth=8, min_samples_leaf=1, n_estimators=2.........
[CV 1/5; 282/400] END max_depth=8, min_samples_leaf=1, n_estimators=2;, score=0.838 total time= 0.0s
[CV 2/5; 282/400] START max_depth=8, min_samples_leaf=1, n_estimators=2.........
[CV 2/5; 282/400] END max_depth=8, min_samples_leaf=1, n_estimators=2;, score=0.833 total time= 0.0s
[CV 3/5; 282/400] START max_depth=8, min_samples_leaf=1, n_estimators=2.........
[CV 3/5; 282/400] END max_depth=8, min_samples_leaf=1, n_estimators=2;, score=0.833 total time= 0.0s
[CV 4/5; 282/400] START max_depth=8, min_samples_leaf=1, n_estimators=2.........
[CV 4/5; 282/400] END max_depth=8, min_samples_leaf=1, n_estimators=2;, score=0.827 total time= 0.0s
[CV 5/5; 282/400] START max_depth=8, min_samples_leaf=1, n_estimators=2.........
[CV 5/5; 282/400] END max_depth=8, min_samples_leaf=1, n_estimators=2;, score=0.809 total time= 0.0s
[CV 1/5; 283/400] START max_depth=8, min_samples_leaf=1, n_estimators=3.........
[CV 1/5; 283/400] END max_depth=8, min_samples_leaf=1, n_estimators=3;, score=0.846 total time= 0.0s
[CV 2/5; 283/400] START max_depth=8, min_samples_leaf=1, n_estimators=3.........
[CV 2/5; 283/400] END max_depth=8, min_samples_leaf=1, n_estimators=3;, score=0.841 total time= 0.0s
[CV 3/5; 283/400] START max_depth=8, min_samples_leaf=1, n_estimators=3.........
[CV 3/5; 283/400] END max_depth=8, min_samples_leaf=1, n_estimators=3;, score=0.838 total time= 0.0s
[CV 4/5; 283/400] START max_depth=8, min_samples_leaf=1, n_estimators=3.........
[CV 4/5; 283/400] END max_depth=8, min_samples_leaf=1, n_estimators=3;, score=0.835 total time= 0.0s
[CV 5/5; 283/400] START max_depth=8, min_samples_leaf=1, n_estimators=3.........
[CV 5/5; 283/400] END max_depth=8, min_samples_leaf=1, n_estimators=3;, score=0.840 total time= 0.0s
[CV 1/5; 284/400] START max_depth=8, min_samples_leaf=1, n_estimators=4.........
[CV 1/5; 284/400] END max_depth=8, min_samples_leaf=1, n_estimators=4;, score=0.854 total time= 0.0s
[CV 2/5; 284/400] START max_depth=8, min_samples_leaf=1, n_estimators=4.........
[CV 2/5; 284/400] END max_depth=8, min_samples_leaf=1, n_estimators=4;, score=0.843 total time= 0.0s
[CV 3/5; 284/400] START max_depth=8, min_samples_leaf=1, n_estimators=4.........
[CV 3/5; 284/400] END max_depth=8, min_samples_leaf=1, n_estimators=4;, score=0.841 total time= 0.0s
[CV 4/5; 284/400] START max_depth=8, min_samples_leaf=1, n_estimators=4.........
[CV 4/5; 284/400] END max_depth=8, min_samples_leaf=1, n_estimators=4;, score=0.835 total time= 0.0s
[CV 5/5; 284/400] START max_depth=8, min_samples_leaf=1, n_estimators=4.........
[CV 5/5; 284/400] END max_depth=8, min_samples_leaf=1, n_estimators=4;, score=0.827 total time= 0.0s
[CV 1/5; 285/400] START max_depth=8, min_samples_leaf=1, n_estimators=5.........
[CV 1/5; 285/400] END max_depth=8, min_samples_leaf=1, n_estimators=5;, score=0.844 total time= 0.0s
[CV 2/5; 285/400] START max_depth=8, min_samples_leaf=1, n_estimators=5.........
[CV 2/5; 285/400] END max_depth=8, min_samples_leaf=1, n_estimators=5;, score=0.841 total time= 0.0s
[CV 3/5; 285/400] START max_depth=8, min_samples_leaf=1, n_estimators=5.........
[CV 3/5; 285/400] END max_depth=8, min_samples_leaf=1, n_estimators=5;, score=0.826 total time= 0.0s
[CV 4/5; 285/400] START max_depth=8, min_samples_leaf=1, n_estimators=5.........
[CV 4/5; 285/400] END max_depth=8, min_samples_leaf=1, n_estimators=5;, score=0.837 total time= 0.0s
[CV 5/5; 285/400] START max_depth=8, min_samples_leaf=1, n_estimators=5.........
[CV 5/5; 285/400] END max_depth=8, min_samples_leaf=1, n_estimators=5;, score=0.835 total time= 0.0s
[CV 1/5; 286/400] START max_depth=8, min_samples_leaf=1, n_estimators=6.........
[CV 1/5; 286/400] END max_depth=8, min_samples_leaf=1, n_estimators=6;, score=0.832 total time= 0.0s
[CV 2/5; 286/400] START max_depth=8, min_samples_leaf=1, n_estimators=6.........
[CV 2/5; 286/400] END max_depth=8, min_samples_leaf=1, n_estimators=6;, score=0.838 total time= 0.0s
[CV 3/5; 286/400] START max_depth=8, min_samples_leaf=1, n_estimators=6.........
[CV 3/5; 286/400] END max_depth=8, min_samples_leaf=1, n_estimators=6;, score=0.838 total time= 0.0s
[CV 4/5; 286/400] START max_depth=8, min_samples_leaf=1, n_estimators=6.........
[CV 4/5; 286/400] END max_depth=8, min_samples_leaf=1, n_estimators=6;, score=0.844 total time= 0.1s
[CV 5/5; 286/400] START max_depth=8, min_samples_leaf=1, n_estimators=6.........
[CV 5/5; 286/400] END max_depth=8, min_samples_leaf=1, n_estimators=6;, score=0.837 total time= 0.1s
[CV 1/5; 287/400] START max_depth=8, min_samples_leaf=1, n_estimators=7.........
[CV 1/5; 287/400] END max_depth=8, min_samples_leaf=1, n_estimators=7;, score=0.843 total time= 0.1s
[CV 2/5; 287/400] START max_depth=8, min_samples_leaf=1, n_estimators=7.........
[CV 2/5; 287/400] END max_depth=8, min_samples_leaf=1, n_estimators=7;, score=0.844 total time= 0.1s
[CV 3/5; 287/400] START max_depth=8, min_samples_leaf=1, n_estimators=7.........
[CV 3/5; 287/400] END max_depth=8, min_samples_leaf=1, n_estimators=7;, score=0.846 total time= 0.1s
[CV 4/5; 287/400] START max_depth=8, min_samples_leaf=1, n_estimators=7.........
[CV 4/5; 287/400] END max_depth=8, min_samples_leaf=1, n_estimators=7;, score=0.843 total time= 0.0s
[CV 5/5; 287/400] START max_depth=8, min_samples_leaf=1, n_estimators=7.........
[CV 5/5; 287/400] END max_depth=8, min_samples_leaf=1, n_estimators=7;, score=0.849 total time= 0.1s
[CV 1/5; 288/400] START max_depth=8, min_samples_leaf=1, n_estimators=8.........
[CV 1/5; 288/400] END max_depth=8, min_samples_leaf=1, n_estimators=8;, score=0.846 total time= 0.1s
[CV 2/5; 288/400] START max_depth=8, min_samples_leaf=1, n_estimators=8.........
[CV 2/5; 288/400] END max_depth=8, min_samples_leaf=1, n_estimators=8;, score=0.851 total time= 0.1s
[CV 3/5; 288/400] START max_depth=8, min_samples_leaf=1, n_estimators=8.........
[CV 3/5; 288/400] END max_depth=8, min_samples_leaf=1, n_estimators=8;, score=0.846 total time= 0.1s
[CV 4/5; 288/400] START max_depth=8, min_samples_leaf=1, n_estimators=8.........
[CV 4/5; 288/400] END max_depth=8, min_samples_leaf=1, n_estimators=8;, score=0.857 total time= 0.1s
[CV 5/5; 288/400] START max_depth=8, min_samples_leaf=1, n_estimators=8.........
[CV 5/5; 288/400] END max_depth=8, min_samples_leaf=1, n_estimators=8;, score=0.846 total time= 0.1s
[CV 1/5; 289/400] START max_depth=8, min_samples_leaf=1, n_estimators=9.........
[CV 1/5; 289/400] END max_depth=8, min_samples_leaf=1, n_estimators=9;, score=0.852 total time= 0.1s
[CV 2/5; 289/400] START max_depth=8, min_samples_leaf=1, n_estimators=9.........
[CV 2/5; 289/400] END max_depth=8, min_samples_leaf=1, n_estimators=9;, score=0.857 total time= 0.1s
[CV 3/5; 289/400] START max_depth=8, min_samples_leaf=1, n_estimators=9.........
[CV 3/5; 289/400] END max_depth=8, min_samples_leaf=1, n_estimators=9;, score=0.849 total time= 0.1s
[CV 4/5; 289/400] START max_depth=8, min_samples_leaf=1, n_estimators=9.........
[CV 4/5; 289/400] END max_depth=8, min_samples_leaf=1, n_estimators=9;, score=0.845 total time= 0.1s
[CV 5/5; 289/400] START max_depth=8, min_samples_leaf=1, n_estimators=9.........
[CV 5/5; 289/400] END max_depth=8, min_samples_leaf=1, n_estimators=9;, score=0.838 total time= 0.1s
[CV 1/5; 290/400] START max_depth=8, min_samples_leaf=1, n_estimators=10........
[CV 1/5; 290/400] END max_depth=8, min_samples_leaf=1, n_estimators=10;, score=0.860 total time= 0.1s
[CV 2/5; 290/400] START max_depth=8, min_samples_leaf=1, n_estimators=10........
[CV 2/5; 290/400] END max_depth=8, min_samples_leaf=1, n_estimators=10;, score=0.843 total time= 0.1s
[CV 3/5; 290/400] START max_depth=8, min_samples_leaf=1, n_estimators=10........
[CV 3/5; 290/400] END max_depth=8, min_samples_leaf=1, n_estimators=10;, score=0.855 total time= 0.1s
[CV 4/5; 290/400] START max_depth=8, min_samples_leaf=1, n_estimators=10........
[CV 4/5; 290/400] END max_depth=8, min_samples_leaf=1, n_estimators=10;, score=0.843 total time= 0.1s
[CV 5/5; 290/400] START max_depth=8, min_samples_leaf=1, n_estimators=10........
[CV 5/5; 290/400] END max_depth=8, min_samples_leaf=1, n_estimators=10;, score=0.852 total time= 0.1s
[CV 1/5; 291/400] START max_depth=8, min_samples_leaf=2, n_estimators=1.........
[CV 1/5; 291/400] END max_depth=8, min_samples_leaf=2, n_estimators=1;, score=0.780 total time= 0.0s
[CV 2/5; 291/400] START max_depth=8, min_samples_leaf=2, n_estimators=1.........
[CV 2/5; 291/400] END max_depth=8, min_samples_leaf=2, n_estimators=1;, score=0.816 total time= 0.0s
[CV 3/5; 291/400] START max_depth=8, min_samples_leaf=2, n_estimators=1.........
[CV 3/5; 291/400] END max_depth=8, min_samples_leaf=2, n_estimators=1;, score=0.807 total time= 0.0s
[CV 4/5; 291/400] START max_depth=8, min_samples_leaf=2, n_estimators=1.........
[CV 4/5; 291/400] END max_depth=8, min_samples_leaf=2, n_estimators=1;, score=0.824 total time= 0.0s
[CV 5/5; 291/400] START max_depth=8, min_samples_leaf=2, n_estimators=1.........
[CV 5/5; 291/400] END max_depth=8, min_samples_leaf=2, n_estimators=1;, score=0.769 total time= 0.0s
[CV 1/5; 292/400] START max_depth=8, min_samples_leaf=2, n_estimators=2.........
[CV 1/5; 292/400] END max_depth=8, min_samples_leaf=2, n_estimators=2;, score=0.817 total time= 0.0s
[CV 2/5; 292/400] START max_depth=8, min_samples_leaf=2, n_estimators=2.........
[CV 2/5; 292/400] END max_depth=8, min_samples_leaf=2, n_estimators=2;, score=0.847 total time= 0.0s
[CV 3/5; 292/400] START max_depth=8, min_samples_leaf=2, n_estimators=2.........
[CV 3/5; 292/400] END max_depth=8, min_samples_leaf=2, n_estimators=2;, score=0.791 total time= 0.0s
[CV 4/5; 292/400] START max_depth=8, min_samples_leaf=2, n_estimators=2.........
[CV 4/5; 292/400] END max_depth=8, min_samples_leaf=2, n_estimators=2;, score=0.807 total time= 0.0s
[CV 5/5; 292/400] START max_depth=8, min_samples_leaf=2, n_estimators=2.........
[CV 5/5; 292/400] END max_depth=8, min_samples_leaf=2, n_estimators=2;, score=0.833 total time= 0.0s
[CV 1/5; 293/400] START max_depth=8, min_samples_leaf=2, n_estimators=3.........
[CV 1/5; 293/400] END max_depth=8, min_samples_leaf=2, n_estimators=3;, score=0.830 total time= 0.0s
[CV 2/5; 293/400] START max_depth=8, min_samples_leaf=2, n_estimators=3.........
[CV 2/5; 293/400] END max_depth=8, min_samples_leaf=2, n_estimators=3;, score=0.839 total time= 0.0s
[CV 3/5; 293/400] START max_depth=8, min_samples_leaf=2, n_estimators=3.........
[CV 3/5; 293/400] END max_depth=8, min_samples_leaf=2, n_estimators=3;, score=0.829 total time= 0.0s
[CV 4/5; 293/400] START max_depth=8, min_samples_leaf=2, n_estimators=3.........
[CV 4/5; 293/400] END max_depth=8, min_samples_leaf=2, n_estimators=3;, score=0.827 total time= 0.0s
[CV 5/5; 293/400] START max_depth=8, min_samples_leaf=2, n_estimators=3.........
[CV 5/5; 293/400] END max_depth=8, min_samples_leaf=2, n_estimators=3;, score=0.829 total time= 0.0s
[CV 1/5; 294/400] START max_depth=8, min_samples_leaf=2, n_estimators=4.........
[CV 1/5; 294/400] END max_depth=8, min_samples_leaf=2, n_estimators=4;, score=0.835 total time= 0.0s
[CV 2/5; 294/400] START max_depth=8, min_samples_leaf=2, n_estimators=4.........
[CV 2/5; 294/400] END max_depth=8, min_samples_leaf=2, n_estimators=4;, score=0.841 total time= 0.0s
[CV 3/5; 294/400] START max_depth=8, min_samples_leaf=2, n_estimators=4.........
[CV 3/5; 294/400] END max_depth=8, min_samples_leaf=2, n_estimators=4;, score=0.829 total time= 0.0s
[CV 4/5; 294/400] START max_depth=8, min_samples_leaf=2, n_estimators=4.........
[CV 4/5; 294/400] END max_depth=8, min_samples_leaf=2, n_estimators=4;, score=0.838 total time= 0.0s
[CV 5/5; 294/400] START max_depth=8, min_samples_leaf=2, n_estimators=4.........
[CV 5/5; 294/400] END max_depth=8, min_samples_leaf=2, n_estimators=4;, score=0.838 total time= 0.0s
[CV 1/5; 295/400] START max_depth=8, min_samples_leaf=2, n_estimators=5.........
[CV 1/5; 295/400] END max_depth=8, min_samples_leaf=2, n_estimators=5;, score=0.857 total time= 0.0s
[CV 2/5; 295/400] START max_depth=8, min_samples_leaf=2, n_estimators=5.........
[CV 2/5; 295/400] END max_depth=8, min_samples_leaf=2, n_estimators=5;, score=0.849 total time= 0.0s
[CV 3/5; 295/400] START max_depth=8, min_samples_leaf=2, n_estimators=5.........
[CV 3/5; 295/400] END max_depth=8, min_samples_leaf=2, n_estimators=5;, score=0.843 total time= 0.0s
[CV 4/5; 295/400] START max_depth=8, min_samples_leaf=2, n_estimators=5.........
[CV 4/5; 295/400] END max_depth=8, min_samples_leaf=2, n_estimators=5;, score=0.845 total time= 0.0s
[CV 5/5; 295/400] START max_depth=8, min_samples_leaf=2, n_estimators=5.........
[CV 5/5; 295/400] END max_depth=8, min_samples_leaf=2, n_estimators=5;, score=0.832 total time= 0.0s
[CV 1/5; 296/400] START max_depth=8, min_samples_leaf=2, n_estimators=6.........
[CV 1/5; 296/400] END max_depth=8, min_samples_leaf=2, n_estimators=6;, score=0.841 total time= 0.0s
[CV 2/5; 296/400] START max_depth=8, min_samples_leaf=2, n_estimators=6.........
[CV 2/5; 296/400] END max_depth=8, min_samples_leaf=2, n_estimators=6;, score=0.831 total time= 0.0s
[CV 3/5; 296/400] START max_depth=8, min_samples_leaf=2, n_estimators=6.........
[CV 3/5; 296/400] END max_depth=8, min_samples_leaf=2, n_estimators=6;, score=0.846 total time= 0.1s
[CV 4/5; 296/400] START max_depth=8, min_samples_leaf=2, n_estimators=6.........
[CV 4/5; 296/400] END max_depth=8, min_samples_leaf=2, n_estimators=6;, score=0.857 total time= 0.0s
[CV 5/5; 296/400] START max_depth=8, min_samples_leaf=2, n_estimators=6.........
[CV 5/5; 296/400] END max_depth=8, min_samples_leaf=2, n_estimators=6;, score=0.843 total time= 0.0s
[CV 1/5; 297/400] START max_depth=8, min_samples_leaf=2, n_estimators=7.........
[CV 1/5; 297/400] END max_depth=8, min_samples_leaf=2, n_estimators=7;, score=0.841 total time= 0.0s
[CV 2/5; 297/400] START max_depth=8, min_samples_leaf=2, n_estimators=7.........
[CV 2/5; 297/400] END max_depth=8, min_samples_leaf=2, n_estimators=7;, score=0.852 total time= 0.0s
[CV 3/5; 297/400] START max_depth=8, min_samples_leaf=2, n_estimators=7.........
[CV 3/5; 297/400] END max_depth=8, min_samples_leaf=2, n_estimators=7;, score=0.846 total time= 0.0s
[CV 4/5; 297/400] START max_depth=8, min_samples_leaf=2, n_estimators=7.........
[CV 4/5; 297/400] END max_depth=8, min_samples_leaf=2, n_estimators=7;, score=0.851 total time= 0.0s
[CV 5/5; 297/400] START max_depth=8, min_samples_leaf=2, n_estimators=7.........
[CV 5/5; 297/400] END max_depth=8, min_samples_leaf=2, n_estimators=7;, score=0.830 total time= 0.0s
[CV 1/5; 298/400] START max_depth=8, min_samples_leaf=2, n_estimators=8.........
[CV 1/5; 298/400] END max_depth=8, min_samples_leaf=2, n_estimators=8;, score=0.849 total time= 0.1s
[CV 2/5; 298/400] START max_depth=8, min_samples_leaf=2, n_estimators=8.........
[CV 2/5; 298/400] END max_depth=8, min_samples_leaf=2, n_estimators=8;, score=0.851 total time= 0.1s
[CV 3/5; 298/400] START max_depth=8, min_samples_leaf=2, n_estimators=8.........
[CV 3/5; 298/400] END max_depth=8, min_samples_leaf=2, n_estimators=8;, score=0.838 total time= 0.1s
[CV 4/5; 298/400] START max_depth=8, min_samples_leaf=2, n_estimators=8.........
[CV 4/5; 298/400] END max_depth=8, min_samples_leaf=2, n_estimators=8;, score=0.843 total time= 0.1s
[CV 5/5; 298/400] START max_depth=8, min_samples_leaf=2, n_estimators=8.........
[CV 5/5; 298/400] END max_depth=8, min_samples_leaf=2, n_estimators=8;, score=0.834 total time= 0.1s
[CV 1/5; 299/400] START max_depth=8, min_samples_leaf=2, n_estimators=9.........
[CV 1/5; 299/400] END max_depth=8, min_samples_leaf=2, n_estimators=9;, score=0.857 total time= 0.1s
[CV 2/5; 299/400] START max_depth=8, min_samples_leaf=2, n_estimators=9.........
[CV 2/5; 299/400] END max_depth=8, min_samples_leaf=2, n_estimators=9;, score=0.856 total time= 0.1s
[CV 3/5; 299/400] START max_depth=8, min_samples_leaf=2, n_estimators=9.........
[CV 3/5; 299/400] END max_depth=8, min_samples_leaf=2, n_estimators=9;, score=0.849 total time= 0.1s
[CV 4/5; 299/400] START max_depth=8, min_samples_leaf=2, n_estimators=9.........
[CV 4/5; 299/400] END max_depth=8, min_samples_leaf=2, n_estimators=9;, score=0.857 total time= 0.1s
[CV 5/5; 299/400] START max_depth=8, min_samples_leaf=2, n_estimators=9.........
[CV 5/5; 299/400] END max_depth=8, min_samples_leaf=2, n_estimators=9;, score=0.851 total time= 0.1s
[CV 1/5; 300/400] START max_depth=8, min_samples_leaf=2, n_estimators=10........
[CV 1/5; 300/400] END max_depth=8, min_samples_leaf=2, n_estimators=10;, score=0.849 total time= 0.1s
[CV 2/5; 300/400] START max_depth=8, min_samples_leaf=2, n_estimators=10........
[CV 2/5; 300/400] END max_depth=8, min_samples_leaf=2, n_estimators=10;, score=0.844 total time= 0.1s
[CV 3/5; 300/400] START max_depth=8, min_samples_leaf=2, n_estimators=10........
[CV 3/5; 300/400] END max_depth=8, min_samples_leaf=2, n_estimators=10;, score=0.843 total time= 0.1s
[CV 4/5; 300/400] START max_depth=8, min_samples_leaf=2, n_estimators=10........
[CV 4/5; 300/400] END max_depth=8, min_samples_leaf=2, n_estimators=10;, score=0.842 total time= 0.1s
[CV 5/5; 300/400] START max_depth=8, min_samples_leaf=2, n_estimators=10........
[CV 5/5; 300/400] END max_depth=8, min_samples_leaf=2, n_estimators=10;, score=0.842 total time= 0.1s
[CV 1/5; 301/400] START max_depth=8, min_samples_leaf=3, n_estimators=1.........
[CV 1/5; 301/400] END max_depth=8, min_samples_leaf=3, n_estimators=1;, score=0.794 total time= 0.0s
[CV 2/5; 301/400] START max_depth=8, min_samples_leaf=3, n_estimators=1.........
[CV 2/5; 301/400] END max_depth=8, min_samples_leaf=3, n_estimators=1;, score=0.791 total time= 0.0s
[CV 3/5; 301/400] START max_depth=8, min_samples_leaf=3, n_estimators=1.........
[CV 3/5; 301/400] END max_depth=8, min_samples_leaf=3, n_estimators=1;, score=0.802 total time= 0.0s
[CV 4/5; 301/400] START max_depth=8, min_samples_leaf=3, n_estimators=1.........
[CV 4/5; 301/400] END max_depth=8, min_samples_leaf=3, n_estimators=1;, score=0.803 total time= 0.0s
[CV 5/5; 301/400] START max_depth=8, min_samples_leaf=3, n_estimators=1.........
[CV 5/5; 301/400] END max_depth=8, min_samples_leaf=3, n_estimators=1;, score=0.804 total time= 0.0s
[CV 1/5; 302/400] START max_depth=8, min_samples_leaf=3, n_estimators=2.........
[CV 1/5; 302/400] END max_depth=8, min_samples_leaf=3, n_estimators=2;, score=0.819 total time= 0.0s
[CV 2/5; 302/400] START max_depth=8, min_samples_leaf=3, n_estimators=2.........
[CV 2/5; 302/400] END max_depth=8, min_samples_leaf=3, n_estimators=2;, score=0.819 total time= 0.0s
[CV 3/5; 302/400] START max_depth=8, min_samples_leaf=3, n_estimators=2.........
[CV 3/5; 302/400] END max_depth=8, min_samples_leaf=3, n_estimators=2;, score=0.836 total time= 0.0s
[CV 4/5; 302/400] START max_depth=8, min_samples_leaf=3, n_estimators=2.........
[CV 4/5; 302/400] END max_depth=8, min_samples_leaf=3, n_estimators=2;, score=0.833 total time= 0.0s
[CV 5/5; 302/400] START max_depth=8, min_samples_leaf=3, n_estimators=2.........
[CV 5/5; 302/400] END max_depth=8, min_samples_leaf=3, n_estimators=2;, score=0.839 total time= 0.0s
[CV 1/5; 303/400] START max_depth=8, min_samples_leaf=3, n_estimators=3.........
[CV 1/5; 303/400] END max_depth=8, min_samples_leaf=3, n_estimators=3;, score=0.828 total time= 0.0s
[CV 2/5; 303/400] START max_depth=8, min_samples_leaf=3, n_estimators=3.........
[CV 2/5; 303/400] END max_depth=8, min_samples_leaf=3, n_estimators=3;, score=0.828 total time= 0.0s
[CV 3/5; 303/400] START max_depth=8, min_samples_leaf=3, n_estimators=3.........
[CV 3/5; 303/400] END max_depth=8, min_samples_leaf=3, n_estimators=3;, score=0.836 total time= 0.0s
[CV 4/5; 303/400] START max_depth=8, min_samples_leaf=3, n_estimators=3.........
[CV 4/5; 303/400] END max_depth=8, min_samples_leaf=3, n_estimators=3;, score=0.835 total time= 0.0s
[CV 5/5; 303/400] START max_depth=8, min_samples_leaf=3, n_estimators=3.........
[CV 5/5; 303/400] END max_depth=8, min_samples_leaf=3, n_estimators=3;, score=0.830 total time= 0.0s
[CV 1/5; 304/400] START max_depth=8, min_samples_leaf=3, n_estimators=4.........
[CV 1/5; 304/400] END max_depth=8, min_samples_leaf=3, n_estimators=4;, score=0.843 total time= 0.0s
[CV 2/5; 304/400] START max_depth=8, min_samples_leaf=3, n_estimators=4.........
[CV 2/5; 304/400] END max_depth=8, min_samples_leaf=3, n_estimators=4;, score=0.848 total time= 0.0s
[CV 3/5; 304/400] START max_depth=8, min_samples_leaf=3, n_estimators=4.........
[CV 3/5; 304/400] END max_depth=8, min_samples_leaf=3, n_estimators=4;, score=0.834 total time= 0.0s
[CV 4/5; 304/400] START max_depth=8, min_samples_leaf=3, n_estimators=4.........
[CV 4/5; 304/400] END max_depth=8, min_samples_leaf=3, n_estimators=4;, score=0.846 total time= 0.0s
[CV 5/5; 304/400] START max_depth=8, min_samples_leaf=3, n_estimators=4.........
[CV 5/5; 304/400] END max_depth=8, min_samples_leaf=3, n_estimators=4;, score=0.826 total time= 0.1s
[CV 1/5; 305/400] START max_depth=8, min_samples_leaf=3, n_estimators=5.........
[CV 1/5; 305/400] END max_depth=8, min_samples_leaf=3, n_estimators=5;, score=0.841 total time= 0.1s
[CV 2/5; 305/400] START max_depth=8, min_samples_leaf=3, n_estimators=5.........
[CV 2/5; 305/400] END max_depth=8, min_samples_leaf=3, n_estimators=5;, score=0.847 total time= 0.1s
[CV 3/5; 305/400] START max_depth=8, min_samples_leaf=3, n_estimators=5.........
[CV 3/5; 305/400] END max_depth=8, min_samples_leaf=3, n_estimators=5;, score=0.832 total time= 0.1s
[CV 4/5; 305/400] START max_depth=8, min_samples_leaf=3, n_estimators=5.........
[CV 4/5; 305/400] END max_depth=8, min_samples_leaf=3, n_estimators=5;, score=0.831 total time= 0.1s
[CV 5/5; 305/400] START max_depth=8, min_samples_leaf=3, n_estimators=5.........
[CV 5/5; 305/400] END max_depth=8, min_samples_leaf=3, n_estimators=5;, score=0.818 total time= 0.1s
[CV 1/5; 306/400] START max_depth=8, min_samples_leaf=3, n_estimators=6.........
[CV 1/5; 306/400] END max_depth=8, min_samples_leaf=3, n_estimators=6;, score=0.838 total time= 0.1s
[CV 2/5; 306/400] START max_depth=8, min_samples_leaf=3, n_estimators=6.........
[CV 2/5; 306/400] END max_depth=8, min_samples_leaf=3, n_estimators=6;, score=0.841 total time= 0.1s
[CV 3/5; 306/400] START max_depth=8, min_samples_leaf=3, n_estimators=6.........
[CV 3/5; 306/400] END max_depth=8, min_samples_leaf=3, n_estimators=6;, score=0.829 total time= 0.1s
[CV 4/5; 306/400] START max_depth=8, min_samples_leaf=3, n_estimators=6.........
[CV 4/5; 306/400] END max_depth=8, min_samples_leaf=3, n_estimators=6;, score=0.849 total time= 0.1s
[CV 5/5; 306/400] START max_depth=8, min_samples_leaf=3, n_estimators=6.........
[CV 5/5; 306/400] END max_depth=8, min_samples_leaf=3, n_estimators=6;, score=0.841 total time= 0.1s
[CV 1/5; 307/400] START max_depth=8, min_samples_leaf=3, n_estimators=7.........
[CV 1/5; 307/400] END max_depth=8, min_samples_leaf=3, n_estimators=7;, score=0.848 total time= 0.1s
[CV 2/5; 307/400] START max_depth=8, min_samples_leaf=3, n_estimators=7.........
[CV 2/5; 307/400] END max_depth=8, min_samples_leaf=3, n_estimators=7;, score=0.830 total time= 0.1s
[CV 3/5; 307/400] START max_depth=8, min_samples_leaf=3, n_estimators=7.........
[CV 3/5; 307/400] END max_depth=8, min_samples_leaf=3, n_estimators=7;, score=0.847 total time= 0.1s
[CV 4/5; 307/400] START max_depth=8, min_samples_leaf=3, n_estimators=7.........
[CV 4/5; 307/400] END max_depth=8, min_samples_leaf=3, n_estimators=7;, score=0.837 total time= 0.1s
[CV 5/5; 307/400] START max_depth=8, min_samples_leaf=3, n_estimators=7.........
[CV 5/5; 307/400] END max_depth=8, min_samples_leaf=3, n_estimators=7;, score=0.838 total time= 0.0s
[CV 1/5; 308/400] START max_depth=8, min_samples_leaf=3, n_estimators=8.........
[CV 1/5; 308/400] END max_depth=8, min_samples_leaf=3, n_estimators=8;, score=0.836 total time= 0.1s
[CV 2/5; 308/400] START max_depth=8, min_samples_leaf=3, n_estimators=8.........
[CV 2/5; 308/400] END max_depth=8, min_samples_leaf=3, n_estimators=8;, score=0.846 total time= 0.1s
[CV 3/5; 308/400] START max_depth=8, min_samples_leaf=3, n_estimators=8.........
[CV 3/5; 308/400] END max_depth=8, min_samples_leaf=3, n_estimators=8;, score=0.837 total time= 0.1s
[CV 4/5; 308/400] START max_depth=8, min_samples_leaf=3, n_estimators=8.........
[CV 4/5; 308/400] END max_depth=8, min_samples_leaf=3, n_estimators=8;, score=0.851 total time= 0.1s
[CV 5/5; 308/400] START max_depth=8, min_samples_leaf=3, n_estimators=8.........
[CV 5/5; 308/400] END max_depth=8, min_samples_leaf=3, n_estimators=8;, score=0.840 total time= 0.1s
[CV 1/5; 309/400] START max_depth=8, min_samples_leaf=3, n_estimators=9.........
[CV 1/5; 309/400] END max_depth=8, min_samples_leaf=3, n_estimators=9;, score=0.839 total time= 0.1s
[CV 2/5; 309/400] START max_depth=8, min_samples_leaf=3, n_estimators=9.........
[CV 2/5; 309/400] END max_depth=8, min_samples_leaf=3, n_estimators=9;, score=0.849 total time= 0.1s
[CV 3/5; 309/400] START max_depth=8, min_samples_leaf=3, n_estimators=9.........
[CV 3/5; 309/400] END max_depth=8, min_samples_leaf=3, n_estimators=9;, score=0.843 total time= 0.1s
[CV 4/5; 309/400] START max_depth=8, min_samples_leaf=3, n_estimators=9.........
[CV 4/5; 309/400] END max_depth=8, min_samples_leaf=3, n_estimators=9;, score=0.851 total time= 0.1s
[CV 5/5; 309/400] START max_depth=8, min_samples_leaf=3, n_estimators=9.........
[CV 5/5; 309/400] END max_depth=8, min_samples_leaf=3, n_estimators=9;, score=0.829 total time= 0.1s
[CV 1/5; 310/400] START max_depth=8, min_samples_leaf=3, n_estimators=10........
[CV 1/5; 310/400] END max_depth=8, min_samples_leaf=3, n_estimators=10;, score=0.846 total time= 0.1s
[CV 2/5; 310/400] START max_depth=8, min_samples_leaf=3, n_estimators=10........
[CV 2/5; 310/400] END max_depth=8, min_samples_leaf=3, n_estimators=10;, score=0.837 total time= 0.1s
[CV 3/5; 310/400] START max_depth=8, min_samples_leaf=3, n_estimators=10........
[CV 3/5; 310/400] END max_depth=8, min_samples_leaf=3, n_estimators=10;, score=0.855 total time= 0.1s
[CV 4/5; 310/400] START max_depth=8, min_samples_leaf=3, n_estimators=10........
[CV 4/5; 310/400] END max_depth=8, min_samples_leaf=3, n_estimators=10;, score=0.833 total time= 0.1s
[CV 5/5; 310/400] START max_depth=8, min_samples_leaf=3, n_estimators=10........
[CV 5/5; 310/400] END max_depth=8, min_samples_leaf=3, n_estimators=10;, score=0.842 total time= 0.1s
[CV 1/5; 311/400] START max_depth=8, min_samples_leaf=4, n_estimators=1.........
[CV 1/5; 311/400] END max_depth=8, min_samples_leaf=4, n_estimators=1;, score=0.803 total time= 0.0s
[CV 2/5; 311/400] START max_depth=8, min_samples_leaf=4, n_estimators=1.........
[CV 2/5; 311/400] END max_depth=8, min_samples_leaf=4, n_estimators=1;, score=0.777 total time= 0.0s
[CV 3/5; 311/400] START max_depth=8, min_samples_leaf=4, n_estimators=1.........
[CV 3/5; 311/400] END max_depth=8, min_samples_leaf=4, n_estimators=1;, score=0.786 total time= 0.0s
[CV 4/5; 311/400] START max_depth=8, min_samples_leaf=4, n_estimators=1.........
[CV 4/5; 311/400] END max_depth=8, min_samples_leaf=4, n_estimators=1;, score=0.796 total time= 0.0s
[CV 5/5; 311/400] START max_depth=8, min_samples_leaf=4, n_estimators=1.........
[CV 5/5; 311/400] END max_depth=8, min_samples_leaf=4, n_estimators=1;, score=0.819 total time= 0.0s
[CV 1/5; 312/400] START max_depth=8, min_samples_leaf=4, n_estimators=2.........
[CV 1/5; 312/400] END max_depth=8, min_samples_leaf=4, n_estimators=2;, score=0.834 total time= 0.0s
[CV 2/5; 312/400] START max_depth=8, min_samples_leaf=4, n_estimators=2.........
[CV 2/5; 312/400] END max_depth=8, min_samples_leaf=4, n_estimators=2;, score=0.825 total time= 0.0s
[CV 3/5; 312/400] START max_depth=8, min_samples_leaf=4, n_estimators=2.........
[CV 3/5; 312/400] END max_depth=8, min_samples_leaf=4, n_estimators=2;, score=0.825 total time= 0.0s
[CV 4/5; 312/400] START max_depth=8, min_samples_leaf=4, n_estimators=2.........
[CV 4/5; 312/400] END max_depth=8, min_samples_leaf=4, n_estimators=2;, score=0.834 total time= 0.0s
[CV 5/5; 312/400] START max_depth=8, min_samples_leaf=4, n_estimators=2.........
[CV 5/5; 312/400] END max_depth=8, min_samples_leaf=4, n_estimators=2;, score=0.821 total time= 0.0s
[CV 1/5; 313/400] START max_depth=8, min_samples_leaf=4, n_estimators=3.........
[CV 1/5; 313/400] END max_depth=8, min_samples_leaf=4, n_estimators=3;, score=0.854 total time= 0.0s
[CV 2/5; 313/400] START max_depth=8, min_samples_leaf=4, n_estimators=3.........
[CV 2/5; 313/400] END max_depth=8, min_samples_leaf=4, n_estimators=3;, score=0.840 total time= 0.0s
[CV 3/5; 313/400] START max_depth=8, min_samples_leaf=4, n_estimators=3.........
[CV 3/5; 313/400] END max_depth=8, min_samples_leaf=4, n_estimators=3;, score=0.829 total time= 0.0s
[CV 4/5; 313/400] START max_depth=8, min_samples_leaf=4, n_estimators=3.........
[CV 4/5; 313/400] END max_depth=8, min_samples_leaf=4, n_estimators=3;, score=0.841 total time= 0.0s
[CV 5/5; 313/400] START max_depth=8, min_samples_leaf=4, n_estimators=3.........
[CV 5/5; 313/400] END max_depth=8, min_samples_leaf=4, n_estimators=3;, score=0.827 total time= 0.0s
[CV 1/5; 314/400] START max_depth=8, min_samples_leaf=4, n_estimators=4.........
[CV 1/5; 314/400] END max_depth=8, min_samples_leaf=4, n_estimators=4;, score=0.838 total time= 0.0s
[CV 2/5; 314/400] START max_depth=8, min_samples_leaf=4, n_estimators=4.........
[CV 2/5; 314/400] END max_depth=8, min_samples_leaf=4, n_estimators=4;, score=0.843 total time= 0.0s
[CV 3/5; 314/400] START max_depth=8, min_samples_leaf=4, n_estimators=4.........
[CV 3/5; 314/400] END max_depth=8, min_samples_leaf=4, n_estimators=4;, score=0.837 total time= 0.0s
[CV 4/5; 314/400] START max_depth=8, min_samples_leaf=4, n_estimators=4.........
[CV 4/5; 314/400] END max_depth=8, min_samples_leaf=4, n_estimators=4;, score=0.842 total time= 0.0s
[CV 5/5; 314/400] START max_depth=8, min_samples_leaf=4, n_estimators=4.........
[CV 5/5; 314/400] END max_depth=8, min_samples_leaf=4, n_estimators=4;, score=0.829 total time= 0.0s
[CV 1/5; 315/400] START max_depth=8, min_samples_leaf=4, n_estimators=5.........
[CV 1/5; 315/400] END max_depth=8, min_samples_leaf=4, n_estimators=5;, score=0.847 total time= 0.0s
[CV 2/5; 315/400] START max_depth=8, min_samples_leaf=4, n_estimators=5.........
[CV 2/5; 315/400] END max_depth=8, min_samples_leaf=4, n_estimators=5;, score=0.846 total time= 0.0s
[CV 3/5; 315/400] START max_depth=8, min_samples_leaf=4, n_estimators=5.........
[CV 3/5; 315/400] END max_depth=8, min_samples_leaf=4, n_estimators=5;, score=0.841 total time= 0.0s
[CV 4/5; 315/400] START max_depth=8, min_samples_leaf=4, n_estimators=5.........
[CV 4/5; 315/400] END max_depth=8, min_samples_leaf=4, n_estimators=5;, score=0.845 total time= 0.0s
[CV 5/5; 315/400] START max_depth=8, min_samples_leaf=4, n_estimators=5.........
[CV 5/5; 315/400] END max_depth=8, min_samples_leaf=4, n_estimators=5;, score=0.841 total time= 0.0s
[CV 1/5; 316/400] START max_depth=8, min_samples_leaf=4, n_estimators=6.........
[CV 1/5; 316/400] END max_depth=8, min_samples_leaf=4, n_estimators=6;, score=0.833 total time= 0.0s
[CV 2/5; 316/400] START max_depth=8, min_samples_leaf=4, n_estimators=6.........
[CV 2/5; 316/400] END max_depth=8, min_samples_leaf=4, n_estimators=6;, score=0.841 total time= 0.0s
[CV 3/5; 316/400] START max_depth=8, min_samples_leaf=4, n_estimators=6.........
[CV 3/5; 316/400] END max_depth=8, min_samples_leaf=4, n_estimators=6;, score=0.841 total time= 0.0s
[CV 4/5; 316/400] START max_depth=8, min_samples_leaf=4, n_estimators=6.........
[CV 4/5; 316/400] END max_depth=8, min_samples_leaf=4, n_estimators=6;, score=0.840 total time= 0.0s
[CV 5/5; 316/400] START max_depth=8, min_samples_leaf=4, n_estimators=6.........
[CV 5/5; 316/400] END max_depth=8, min_samples_leaf=4, n_estimators=6;, score=0.840 total time= 0.0s
[CV 1/5; 317/400] START max_depth=8, min_samples_leaf=4, n_estimators=7.........
[CV 1/5; 317/400] END max_depth=8, min_samples_leaf=4, n_estimators=7;, score=0.843 total time= 0.1s
[CV 2/5; 317/400] START max_depth=8, min_samples_leaf=4, n_estimators=7.........
[CV 2/5; 317/400] END max_depth=8, min_samples_leaf=4, n_estimators=7;, score=0.838 total time= 0.0s
[CV 3/5; 317/400] START max_depth=8, min_samples_leaf=4, n_estimators=7.........
[CV 3/5; 317/400] END max_depth=8, min_samples_leaf=4, n_estimators=7;, score=0.843 total time= 0.0s
[CV 4/5; 317/400] START max_depth=8, min_samples_leaf=4, n_estimators=7.........
[CV 4/5; 317/400] END max_depth=8, min_samples_leaf=4, n_estimators=7;, score=0.834 total time= 0.0s
[CV 5/5; 317/400] START max_depth=8, min_samples_leaf=4, n_estimators=7.........
[CV 5/5; 317/400] END max_depth=8, min_samples_leaf=4, n_estimators=7;, score=0.849 total time= 0.1s
[CV 1/5; 318/400] START max_depth=8, min_samples_leaf=4, n_estimators=8.........
[CV 1/5; 318/400] END max_depth=8, min_samples_leaf=4, n_estimators=8;, score=0.844 total time= 0.1s
[CV 2/5; 318/400] START max_depth=8, min_samples_leaf=4, n_estimators=8.........
[CV 2/5; 318/400] END max_depth=8, min_samples_leaf=4, n_estimators=8;, score=0.839 total time= 0.1s
[CV 3/5; 318/400] START max_depth=8, min_samples_leaf=4, n_estimators=8.........
[CV 3/5; 318/400] END max_depth=8, min_samples_leaf=4, n_estimators=8;, score=0.836 total time= 0.1s
[CV 4/5; 318/400] START max_depth=8, min_samples_leaf=4, n_estimators=8.........
[CV 4/5; 318/400] END max_depth=8, min_samples_leaf=4, n_estimators=8;, score=0.846 total time= 0.1s
[CV 5/5; 318/400] START max_depth=8, min_samples_leaf=4, n_estimators=8.........
[CV 5/5; 318/400] END max_depth=8, min_samples_leaf=4, n_estimators=8;, score=0.839 total time= 0.1s
[CV 1/5; 319/400] START max_depth=8, min_samples_leaf=4, n_estimators=9.........
[CV 1/5; 319/400] END max_depth=8, min_samples_leaf=4, n_estimators=9;, score=0.851 total time= 0.1s
[CV 2/5; 319/400] START max_depth=8, min_samples_leaf=4, n_estimators=9.........
[CV 2/5; 319/400] END max_depth=8, min_samples_leaf=4, n_estimators=9;, score=0.854 total time= 0.1s
[CV 3/5; 319/400] START max_depth=8, min_samples_leaf=4, n_estimators=9.........
[CV 3/5; 319/400] END max_depth=8, min_samples_leaf=4, n_estimators=9;, score=0.841 total time= 0.1s
[CV 4/5; 319/400] START max_depth=8, min_samples_leaf=4, n_estimators=9.........
[CV 4/5; 319/400] END max_depth=8, min_samples_leaf=4, n_estimators=9;, score=0.845 total time= 0.1s
[CV 5/5; 319/400] START max_depth=8, min_samples_leaf=4, n_estimators=9.........
[CV 5/5; 319/400] END max_depth=8, min_samples_leaf=4, n_estimators=9;, score=0.842 total time= 0.1s
[CV 1/5; 320/400] START max_depth=8, min_samples_leaf=4, n_estimators=10........
[CV 1/5; 320/400] END max_depth=8, min_samples_leaf=4, n_estimators=10;, score=0.847 total time= 0.1s
[CV 2/5; 320/400] START max_depth=8, min_samples_leaf=4, n_estimators=10........
[CV 2/5; 320/400] END max_depth=8, min_samples_leaf=4, n_estimators=10;, score=0.835 total time= 0.1s
[CV 3/5; 320/400] START max_depth=8, min_samples_leaf=4, n_estimators=10........
[CV 3/5; 320/400] END max_depth=8, min_samples_leaf=4, n_estimators=10;, score=0.844 total time= 0.1s
[CV 4/5; 320/400] START max_depth=8, min_samples_leaf=4, n_estimators=10........
[CV 4/5; 320/400] END max_depth=8, min_samples_leaf=4, n_estimators=10;, score=0.848 total time= 0.1s
[CV 5/5; 320/400] START max_depth=8, min_samples_leaf=4, n_estimators=10........
[CV 5/5; 320/400] END max_depth=8, min_samples_leaf=4, n_estimators=10;, score=0.841 total time= 0.1s
[CV 1/5; 321/400] START max_depth=9, min_samples_leaf=1, n_estimators=1.........
[CV 1/5; 321/400] END max_depth=9, min_samples_leaf=1, n_estimators=1;, score=0.843 total time= 0.0s
[CV 2/5; 321/400] START max_depth=9, min_samples_leaf=1, n_estimators=1.........
[CV 2/5; 321/400] END max_depth=9, min_samples_leaf=1, n_estimators=1;, score=0.822 total time= 0.0s
[CV 3/5; 321/400] START max_depth=9, min_samples_leaf=1, n_estimators=1.........
[CV 3/5; 321/400] END max_depth=9, min_samples_leaf=1, n_estimators=1;, score=0.831 total time= 0.0s
[CV 4/5; 321/400] START max_depth=9, min_samples_leaf=1, n_estimators=1.........
[CV 4/5; 321/400] END max_depth=9, min_samples_leaf=1, n_estimators=1;, score=0.773 total time= 0.0s
[CV 5/5; 321/400] START max_depth=9, min_samples_leaf=1, n_estimators=1.........
[CV 5/5; 321/400] END max_depth=9, min_samples_leaf=1, n_estimators=1;, score=0.798 total time= 0.0s
[CV 1/5; 322/400] START max_depth=9, min_samples_leaf=1, n_estimators=2.........
[CV 1/5; 322/400] END max_depth=9, min_samples_leaf=1, n_estimators=2;, score=0.821 total time= 0.0s
[CV 2/5; 322/400] START max_depth=9, min_samples_leaf=1, n_estimators=2.........
[CV 2/5; 322/400] END max_depth=9, min_samples_leaf=1, n_estimators=2;, score=0.833 total time= 0.0s
[CV 3/5; 322/400] START max_depth=9, min_samples_leaf=1, n_estimators=2.........
[CV 3/5; 322/400] END max_depth=9, min_samples_leaf=1, n_estimators=2;, score=0.834 total time= 0.0s
[CV 4/5; 322/400] START max_depth=9, min_samples_leaf=1, n_estimators=2.........
[CV 4/5; 322/400] END max_depth=9, min_samples_leaf=1, n_estimators=2;, score=0.844 total time= 0.0s
[CV 5/5; 322/400] START max_depth=9, min_samples_leaf=1, n_estimators=2.........
[CV 5/5; 322/400] END max_depth=9, min_samples_leaf=1, n_estimators=2;, score=0.832 total time= 0.0s
[CV 1/5; 323/400] START max_depth=9, min_samples_leaf=1, n_estimators=3.........
[CV 1/5; 323/400] END max_depth=9, min_samples_leaf=1, n_estimators=3;, score=0.851 total time= 0.0s
[CV 2/5; 323/400] START max_depth=9, min_samples_leaf=1, n_estimators=3.........
[CV 2/5; 323/400] END max_depth=9, min_samples_leaf=1, n_estimators=3;, score=0.843 total time= 0.0s
[CV 3/5; 323/400] START max_depth=9, min_samples_leaf=1, n_estimators=3.........
[CV 3/5; 323/400] END max_depth=9, min_samples_leaf=1, n_estimators=3;, score=0.841 total time= 0.0s
[CV 4/5; 323/400] START max_depth=9, min_samples_leaf=1, n_estimators=3.........
[CV 4/5; 323/400] END max_depth=9, min_samples_leaf=1, n_estimators=3;, score=0.847 total time= 0.0s
[CV 5/5; 323/400] START max_depth=9, min_samples_leaf=1, n_estimators=3.........
[CV 5/5; 323/400] END max_depth=9, min_samples_leaf=1, n_estimators=3;, score=0.844 total time= 0.0s
[CV 1/5; 324/400] START max_depth=9, min_samples_leaf=1, n_estimators=4.........
[CV 1/5; 324/400] END max_depth=9, min_samples_leaf=1, n_estimators=4;, score=0.858 total time= 0.0s
[CV 2/5; 324/400] START max_depth=9, min_samples_leaf=1, n_estimators=4.........
[CV 2/5; 324/400] END max_depth=9, min_samples_leaf=1, n_estimators=4;, score=0.857 total time= 0.0s
[CV 3/5; 324/400] START max_depth=9, min_samples_leaf=1, n_estimators=4.........
[CV 3/5; 324/400] END max_depth=9, min_samples_leaf=1, n_estimators=4;, score=0.844 total time= 0.0s
[CV 4/5; 324/400] START max_depth=9, min_samples_leaf=1, n_estimators=4.........
[CV 4/5; 324/400] END max_depth=9, min_samples_leaf=1, n_estimators=4;, score=0.857 total time= 0.0s
[CV 5/5; 324/400] START max_depth=9, min_samples_leaf=1, n_estimators=4.........
[CV 5/5; 324/400] END max_depth=9, min_samples_leaf=1, n_estimators=4;, score=0.852 total time= 0.0s
[CV 1/5; 325/400] START max_depth=9, min_samples_leaf=1, n_estimators=5.........
[CV 1/5; 325/400] END max_depth=9, min_samples_leaf=1, n_estimators=5;, score=0.846 total time= 0.0s
[CV 2/5; 325/400] START max_depth=9, min_samples_leaf=1, n_estimators=5.........
[CV 2/5; 325/400] END max_depth=9, min_samples_leaf=1, n_estimators=5;, score=0.851 total time= 0.0s
[CV 3/5; 325/400] START max_depth=9, min_samples_leaf=1, n_estimators=5.........
[CV 3/5; 325/400] END max_depth=9, min_samples_leaf=1, n_estimators=5;, score=0.861 total time= 0.0s
[CV 4/5; 325/400] START max_depth=9, min_samples_leaf=1, n_estimators=5.........
[CV 4/5; 325/400] END max_depth=9, min_samples_leaf=1, n_estimators=5;, score=0.849 total time= 0.0s
[CV 5/5; 325/400] START max_depth=9, min_samples_leaf=1, n_estimators=5.........
[CV 5/5; 325/400] END max_depth=9, min_samples_leaf=1, n_estimators=5;, score=0.860 total time= 0.0s
[CV 1/5; 326/400] START max_depth=9, min_samples_leaf=1, n_estimators=6.........
[CV 1/5; 326/400] END max_depth=9, min_samples_leaf=1, n_estimators=6;, score=0.855 total time= 0.0s
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[CV 2/5; 326/400] END max_depth=9, min_samples_leaf=1, n_estimators=6;, score=0.860 total time= 0.0s
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[CV 3/5; 326/400] END max_depth=9, min_samples_leaf=1, n_estimators=6;, score=0.851 total time= 0.0s
[CV 4/5; 326/400] START max_depth=9, min_samples_leaf=1, n_estimators=6.........
[CV 4/5; 326/400] END max_depth=9, min_samples_leaf=1, n_estimators=6;, score=0.859 total time= 0.1s
[CV 5/5; 326/400] START max_depth=9, min_samples_leaf=1, n_estimators=6.........
[CV 5/5; 326/400] END max_depth=9, min_samples_leaf=1, n_estimators=6;, score=0.848 total time= 0.0s
[CV 1/5; 327/400] START max_depth=9, min_samples_leaf=1, n_estimators=7.........
[CV 1/5; 327/400] END max_depth=9, min_samples_leaf=1, n_estimators=7;, score=0.868 total time= 0.1s
[CV 2/5; 327/400] START max_depth=9, min_samples_leaf=1, n_estimators=7.........
[CV 2/5; 327/400] END max_depth=9, min_samples_leaf=1, n_estimators=7;, score=0.849 total time= 0.1s
[CV 3/5; 327/400] START max_depth=9, min_samples_leaf=1, n_estimators=7.........
[CV 3/5; 327/400] END max_depth=9, min_samples_leaf=1, n_estimators=7;, score=0.858 total time= 0.1s
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[CV 4/5; 327/400] END max_depth=9, min_samples_leaf=1, n_estimators=7;, score=0.868 total time= 0.1s
[CV 5/5; 327/400] START max_depth=9, min_samples_leaf=1, n_estimators=7.........
[CV 5/5; 327/400] END max_depth=9, min_samples_leaf=1, n_estimators=7;, score=0.862 total time= 0.1s
[CV 1/5; 328/400] START max_depth=9, min_samples_leaf=1, n_estimators=8.........
[CV 1/5; 328/400] END max_depth=9, min_samples_leaf=1, n_estimators=8;, score=0.865 total time= 0.1s
[CV 2/5; 328/400] START max_depth=9, min_samples_leaf=1, n_estimators=8.........
[CV 2/5; 328/400] END max_depth=9, min_samples_leaf=1, n_estimators=8;, score=0.864 total time= 0.1s
[CV 3/5; 328/400] START max_depth=9, min_samples_leaf=1, n_estimators=8.........
[CV 3/5; 328/400] END max_depth=9, min_samples_leaf=1, n_estimators=8;, score=0.860 total time= 0.1s
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[CV 4/5; 328/400] END max_depth=9, min_samples_leaf=1, n_estimators=8;, score=0.855 total time= 0.1s
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[CV 5/5; 328/400] END max_depth=9, min_samples_leaf=1, n_estimators=8;, score=0.852 total time= 0.1s
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[CV 1/5; 329/400] END max_depth=9, min_samples_leaf=1, n_estimators=9;, score=0.855 total time= 0.1s
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[CV 2/5; 329/400] END max_depth=9, min_samples_leaf=1, n_estimators=9;, score=0.862 total time= 0.1s
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[CV 3/5; 329/400] END max_depth=9, min_samples_leaf=1, n_estimators=9;, score=0.854 total time= 0.1s
[CV 4/5; 329/400] START max_depth=9, min_samples_leaf=1, n_estimators=9.........
[CV 4/5; 329/400] END max_depth=9, min_samples_leaf=1, n_estimators=9;, score=0.863 total time= 0.1s
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[CV 5/5; 329/400] END max_depth=9, min_samples_leaf=1, n_estimators=9;, score=0.852 total time= 0.1s
[CV 1/5; 330/400] START max_depth=9, min_samples_leaf=1, n_estimators=10........
[CV 1/5; 330/400] END max_depth=9, min_samples_leaf=1, n_estimators=10;, score=0.867 total time= 0.1s
[CV 2/5; 330/400] START max_depth=9, min_samples_leaf=1, n_estimators=10........
[CV 2/5; 330/400] END max_depth=9, min_samples_leaf=1, n_estimators=10;, score=0.863 total time= 0.1s
[CV 3/5; 330/400] START max_depth=9, min_samples_leaf=1, n_estimators=10........
[CV 3/5; 330/400] END max_depth=9, min_samples_leaf=1, n_estimators=10;, score=0.850 total time= 0.1s
[CV 4/5; 330/400] START max_depth=9, min_samples_leaf=1, n_estimators=10........
[CV 4/5; 330/400] END max_depth=9, min_samples_leaf=1, n_estimators=10;, score=0.866 total time= 0.1s
[CV 5/5; 330/400] START max_depth=9, min_samples_leaf=1, n_estimators=10........
[CV 5/5; 330/400] END max_depth=9, min_samples_leaf=1, n_estimators=10;, score=0.851 total time= 0.1s
[CV 1/5; 331/400] START max_depth=9, min_samples_leaf=2, n_estimators=1.........
[CV 1/5; 331/400] END max_depth=9, min_samples_leaf=2, n_estimators=1;, score=0.795 total time= 0.0s
[CV 2/5; 331/400] START max_depth=9, min_samples_leaf=2, n_estimators=1.........
[CV 2/5; 331/400] END max_depth=9, min_samples_leaf=2, n_estimators=1;, score=0.783 total time= 0.0s
[CV 3/5; 331/400] START max_depth=9, min_samples_leaf=2, n_estimators=1.........
[CV 3/5; 331/400] END max_depth=9, min_samples_leaf=2, n_estimators=1;, score=0.807 total time= 0.0s
[CV 4/5; 331/400] START max_depth=9, min_samples_leaf=2, n_estimators=1.........
[CV 4/5; 331/400] END max_depth=9, min_samples_leaf=2, n_estimators=1;, score=0.831 total time= 0.0s
[CV 5/5; 331/400] START max_depth=9, min_samples_leaf=2, n_estimators=1.........
[CV 5/5; 331/400] END max_depth=9, min_samples_leaf=2, n_estimators=1;, score=0.782 total time= 0.0s
[CV 1/5; 332/400] START max_depth=9, min_samples_leaf=2, n_estimators=2.........
[CV 1/5; 332/400] END max_depth=9, min_samples_leaf=2, n_estimators=2;, score=0.827 total time= 0.0s
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[CV 2/5; 332/400] END max_depth=9, min_samples_leaf=2, n_estimators=2;, score=0.835 total time= 0.0s
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[CV 3/5; 332/400] END max_depth=9, min_samples_leaf=2, n_estimators=2;, score=0.822 total time= 0.0s
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[CV 4/5; 332/400] END max_depth=9, min_samples_leaf=2, n_estimators=2;, score=0.846 total time= 0.0s
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[CV 5/5; 332/400] END max_depth=9, min_samples_leaf=2, n_estimators=2;, score=0.827 total time= 0.0s
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[CV 1/5; 333/400] END max_depth=9, min_samples_leaf=2, n_estimators=3;, score=0.843 total time= 0.0s
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[CV 2/5; 333/400] END max_depth=9, min_samples_leaf=2, n_estimators=3;, score=0.842 total time= 0.0s
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[CV 3/5; 333/400] END max_depth=9, min_samples_leaf=2, n_estimators=3;, score=0.849 total time= 0.0s
[CV 4/5; 333/400] START max_depth=9, min_samples_leaf=2, n_estimators=3.........
[CV 4/5; 333/400] END max_depth=9, min_samples_leaf=2, n_estimators=3;, score=0.841 total time= 0.0s
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[CV 5/5; 333/400] END max_depth=9, min_samples_leaf=2, n_estimators=3;, score=0.835 total time= 0.0s
[CV 1/5; 334/400] START max_depth=9, min_samples_leaf=2, n_estimators=4.........
[CV 1/5; 334/400] END max_depth=9, min_samples_leaf=2, n_estimators=4;, score=0.843 total time= 0.0s
[CV 2/5; 334/400] START max_depth=9, min_samples_leaf=2, n_estimators=4.........
[CV 2/5; 334/400] END max_depth=9, min_samples_leaf=2, n_estimators=4;, score=0.839 total time= 0.0s
[CV 3/5; 334/400] START max_depth=9, min_samples_leaf=2, n_estimators=4.........
[CV 3/5; 334/400] END max_depth=9, min_samples_leaf=2, n_estimators=4;, score=0.853 total time= 0.0s
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[CV 4/5; 334/400] END max_depth=9, min_samples_leaf=2, n_estimators=4;, score=0.845 total time= 0.0s
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[CV 5/5; 334/400] END max_depth=9, min_samples_leaf=2, n_estimators=4;, score=0.852 total time= 0.0s
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[CV 1/5; 335/400] END max_depth=9, min_samples_leaf=2, n_estimators=5;, score=0.858 total time= 0.1s
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[CV 2/5; 335/400] END max_depth=9, min_samples_leaf=2, n_estimators=5;, score=0.843 total time= 0.0s
[CV 3/5; 335/400] START max_depth=9, min_samples_leaf=2, n_estimators=5.........
[CV 3/5; 335/400] END max_depth=9, min_samples_leaf=2, n_estimators=5;, score=0.847 total time= 0.0s
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[CV 4/5; 335/400] END max_depth=9, min_samples_leaf=2, n_estimators=5;, score=0.850 total time= 0.0s
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[CV 5/5; 335/400] END max_depth=9, min_samples_leaf=2, n_estimators=5;, score=0.846 total time= 0.0s
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[CV 1/5; 336/400] END max_depth=9, min_samples_leaf=2, n_estimators=6;, score=0.861 total time= 0.1s
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[CV 2/5; 336/400] END max_depth=9, min_samples_leaf=2, n_estimators=6;, score=0.843 total time= 0.1s
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[CV 3/5; 336/400] END max_depth=9, min_samples_leaf=2, n_estimators=6;, score=0.850 total time= 0.1s
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[CV 4/5; 336/400] END max_depth=9, min_samples_leaf=2, n_estimators=6;, score=0.852 total time= 0.1s
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[CV 5/5; 336/400] END max_depth=9, min_samples_leaf=2, n_estimators=6;, score=0.865 total time= 0.1s
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[CV 1/5; 337/400] END max_depth=9, min_samples_leaf=2, n_estimators=7;, score=0.863 total time= 0.1s
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[CV 2/5; 337/400] END max_depth=9, min_samples_leaf=2, n_estimators=7;, score=0.855 total time= 0.1s
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[CV 3/5; 337/400] END max_depth=9, min_samples_leaf=2, n_estimators=7;, score=0.863 total time= 0.1s
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[CV 4/5; 337/400] END max_depth=9, min_samples_leaf=2, n_estimators=7;, score=0.843 total time= 0.1s
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[CV 5/5; 337/400] END max_depth=9, min_samples_leaf=2, n_estimators=7;, score=0.862 total time= 0.1s
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[CV 1/5; 338/400] END max_depth=9, min_samples_leaf=2, n_estimators=8;, score=0.856 total time= 0.1s
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[CV 2/5; 338/400] END max_depth=9, min_samples_leaf=2, n_estimators=8;, score=0.857 total time= 0.1s
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[CV 3/5; 338/400] END max_depth=9, min_samples_leaf=2, n_estimators=8;, score=0.860 total time= 0.1s
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[CV 4/5; 338/400] END max_depth=9, min_samples_leaf=2, n_estimators=8;, score=0.863 total time= 0.1s
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[CV 5/5; 338/400] END max_depth=9, min_samples_leaf=2, n_estimators=8;, score=0.860 total time= 0.1s
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[CV 1/5; 339/400] END max_depth=9, min_samples_leaf=2, n_estimators=9;, score=0.853 total time= 0.1s
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[CV 2/5; 339/400] END max_depth=9, min_samples_leaf=2, n_estimators=9;, score=0.862 total time= 0.1s
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[CV 3/5; 339/400] END max_depth=9, min_samples_leaf=2, n_estimators=9;, score=0.852 total time= 0.1s
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[CV 4/5; 339/400] END max_depth=9, min_samples_leaf=2, n_estimators=9;, score=0.867 total time= 0.1s
[CV 5/5; 339/400] START max_depth=9, min_samples_leaf=2, n_estimators=9.........
[CV 5/5; 339/400] END max_depth=9, min_samples_leaf=2, n_estimators=9;, score=0.861 total time= 0.1s
[CV 1/5; 340/400] START max_depth=9, min_samples_leaf=2, n_estimators=10........
[CV 1/5; 340/400] END max_depth=9, min_samples_leaf=2, n_estimators=10;, score=0.858 total time= 0.1s
[CV 2/5; 340/400] START max_depth=9, min_samples_leaf=2, n_estimators=10........
[CV 2/5; 340/400] END max_depth=9, min_samples_leaf=2, n_estimators=10;, score=0.857 total time= 0.1s
[CV 3/5; 340/400] START max_depth=9, min_samples_leaf=2, n_estimators=10........
[CV 3/5; 340/400] END max_depth=9, min_samples_leaf=2, n_estimators=10;, score=0.862 total time= 0.1s
[CV 4/5; 340/400] START max_depth=9, min_samples_leaf=2, n_estimators=10........
[CV 4/5; 340/400] END max_depth=9, min_samples_leaf=2, n_estimators=10;, score=0.861 total time= 0.1s
[CV 5/5; 340/400] START max_depth=9, min_samples_leaf=2, n_estimators=10........
[CV 5/5; 340/400] END max_depth=9, min_samples_leaf=2, n_estimators=10;, score=0.867 total time= 0.1s
[CV 1/5; 341/400] START max_depth=9, min_samples_leaf=3, n_estimators=1.........
[CV 1/5; 341/400] END max_depth=9, min_samples_leaf=3, n_estimators=1;, score=0.789 total time= 0.0s
[CV 2/5; 341/400] START max_depth=9, min_samples_leaf=3, n_estimators=1.........
[CV 2/5; 341/400] END max_depth=9, min_samples_leaf=3, n_estimators=1;, score=0.810 total time= 0.0s
[CV 3/5; 341/400] START max_depth=9, min_samples_leaf=3, n_estimators=1.........
[CV 3/5; 341/400] END max_depth=9, min_samples_leaf=3, n_estimators=1;, score=0.810 total time= 0.0s
[CV 4/5; 341/400] START max_depth=9, min_samples_leaf=3, n_estimators=1.........
[CV 4/5; 341/400] END max_depth=9, min_samples_leaf=3, n_estimators=1;, score=0.818 total time= 0.0s
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[CV 5/5; 341/400] END max_depth=9, min_samples_leaf=3, n_estimators=1;, score=0.786 total time= 0.0s
[CV 1/5; 342/400] START max_depth=9, min_samples_leaf=3, n_estimators=2.........
[CV 1/5; 342/400] END max_depth=9, min_samples_leaf=3, n_estimators=2;, score=0.838 total time= 0.0s
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[CV 2/5; 342/400] END max_depth=9, min_samples_leaf=3, n_estimators=2;, score=0.835 total time= 0.0s
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[CV 3/5; 342/400] END max_depth=9, min_samples_leaf=3, n_estimators=2;, score=0.840 total time= 0.0s
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[CV 4/5; 342/400] END max_depth=9, min_samples_leaf=3, n_estimators=2;, score=0.842 total time= 0.0s
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[CV 5/5; 342/400] END max_depth=9, min_samples_leaf=3, n_estimators=2;, score=0.843 total time= 0.0s
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[CV 1/5; 343/400] END max_depth=9, min_samples_leaf=3, n_estimators=3;, score=0.841 total time= 0.0s
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[CV 2/5; 343/400] END max_depth=9, min_samples_leaf=3, n_estimators=3;, score=0.838 total time= 0.0s
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[CV 3/5; 343/400] END max_depth=9, min_samples_leaf=3, n_estimators=3;, score=0.838 total time= 0.0s
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[CV 4/5; 343/400] END max_depth=9, min_samples_leaf=3, n_estimators=3;, score=0.841 total time= 0.0s
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[CV 5/5; 343/400] END max_depth=9, min_samples_leaf=3, n_estimators=3;, score=0.835 total time= 0.0s
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[CV 1/5; 344/400] END max_depth=9, min_samples_leaf=3, n_estimators=4;, score=0.856 total time= 0.0s
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[CV 2/5; 344/400] END max_depth=9, min_samples_leaf=3, n_estimators=4;, score=0.832 total time= 0.0s
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[CV 3/5; 344/400] END max_depth=9, min_samples_leaf=3, n_estimators=4;, score=0.846 total time= 0.0s
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[CV 4/5; 344/400] END max_depth=9, min_samples_leaf=3, n_estimators=4;, score=0.857 total time= 0.0s
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[CV 5/5; 344/400] END max_depth=9, min_samples_leaf=3, n_estimators=4;, score=0.838 total time= 0.0s
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[CV 1/5; 345/400] END max_depth=9, min_samples_leaf=3, n_estimators=5;, score=0.849 total time= 0.0s
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[CV 2/5; 345/400] END max_depth=9, min_samples_leaf=3, n_estimators=5;, score=0.860 total time= 0.0s
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[CV 3/5; 345/400] END max_depth=9, min_samples_leaf=3, n_estimators=5;, score=0.846 total time= 0.0s
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[CV 4/5; 345/400] END max_depth=9, min_samples_leaf=3, n_estimators=5;, score=0.855 total time= 0.0s
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[CV 5/5; 345/400] END max_depth=9, min_samples_leaf=3, n_estimators=5;, score=0.862 total time= 0.0s
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[CV 1/5; 346/400] END max_depth=9, min_samples_leaf=3, n_estimators=6;, score=0.852 total time= 0.1s
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[CV 2/5; 346/400] END max_depth=9, min_samples_leaf=3, n_estimators=6;, score=0.849 total time= 0.0s
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[CV 3/5; 346/400] END max_depth=9, min_samples_leaf=3, n_estimators=6;, score=0.842 total time= 0.0s
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[CV 4/5; 346/400] END max_depth=9, min_samples_leaf=3, n_estimators=6;, score=0.852 total time= 0.0s
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[CV 5/5; 346/400] END max_depth=9, min_samples_leaf=3, n_estimators=6;, score=0.863 total time= 0.0s
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[CV 1/5; 347/400] END max_depth=9, min_samples_leaf=3, n_estimators=7;, score=0.863 total time= 0.1s
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[CV 2/5; 347/400] END max_depth=9, min_samples_leaf=3, n_estimators=7;, score=0.845 total time= 0.0s
[CV 3/5; 347/400] START max_depth=9, min_samples_leaf=3, n_estimators=7.........
[CV 3/5; 347/400] END max_depth=9, min_samples_leaf=3, n_estimators=7;, score=0.851 total time= 0.1s
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[CV 4/5; 347/400] END max_depth=9, min_samples_leaf=3, n_estimators=7;, score=0.857 total time= 0.1s
[CV 5/5; 347/400] START max_depth=9, min_samples_leaf=3, n_estimators=7.........
[CV 5/5; 347/400] END max_depth=9, min_samples_leaf=3, n_estimators=7;, score=0.862 total time= 0.1s
[CV 1/5; 348/400] START max_depth=9, min_samples_leaf=3, n_estimators=8.........
[CV 1/5; 348/400] END max_depth=9, min_samples_leaf=3, n_estimators=8;, score=0.856 total time= 0.1s
[CV 2/5; 348/400] START max_depth=9, min_samples_leaf=3, n_estimators=8.........
[CV 2/5; 348/400] END max_depth=9, min_samples_leaf=3, n_estimators=8;, score=0.854 total time= 0.1s
[CV 3/5; 348/400] START max_depth=9, min_samples_leaf=3, n_estimators=8.........
[CV 3/5; 348/400] END max_depth=9, min_samples_leaf=3, n_estimators=8;, score=0.859 total time= 0.1s
[CV 4/5; 348/400] START max_depth=9, min_samples_leaf=3, n_estimators=8.........
[CV 4/5; 348/400] END max_depth=9, min_samples_leaf=3, n_estimators=8;, score=0.870 total time= 0.1s
[CV 5/5; 348/400] START max_depth=9, min_samples_leaf=3, n_estimators=8.........
[CV 5/5; 348/400] END max_depth=9, min_samples_leaf=3, n_estimators=8;, score=0.853 total time= 0.1s
[CV 1/5; 349/400] START max_depth=9, min_samples_leaf=3, n_estimators=9.........
[CV 1/5; 349/400] END max_depth=9, min_samples_leaf=3, n_estimators=9;, score=0.860 total time= 0.1s
[CV 2/5; 349/400] START max_depth=9, min_samples_leaf=3, n_estimators=9.........
[CV 2/5; 349/400] END max_depth=9, min_samples_leaf=3, n_estimators=9;, score=0.855 total time= 0.1s
[CV 3/5; 349/400] START max_depth=9, min_samples_leaf=3, n_estimators=9.........
[CV 3/5; 349/400] END max_depth=9, min_samples_leaf=3, n_estimators=9;, score=0.855 total time= 0.1s
[CV 4/5; 349/400] START max_depth=9, min_samples_leaf=3, n_estimators=9.........
[CV 4/5; 349/400] END max_depth=9, min_samples_leaf=3, n_estimators=9;, score=0.857 total time= 0.1s
[CV 5/5; 349/400] START max_depth=9, min_samples_leaf=3, n_estimators=9.........
[CV 5/5; 349/400] END max_depth=9, min_samples_leaf=3, n_estimators=9;, score=0.852 total time= 0.1s
[CV 1/5; 350/400] START max_depth=9, min_samples_leaf=3, n_estimators=10........
[CV 1/5; 350/400] END max_depth=9, min_samples_leaf=3, n_estimators=10;, score=0.856 total time= 0.1s
[CV 2/5; 350/400] START max_depth=9, min_samples_leaf=3, n_estimators=10........
[CV 2/5; 350/400] END max_depth=9, min_samples_leaf=3, n_estimators=10;, score=0.857 total time= 0.1s
[CV 3/5; 350/400] START max_depth=9, min_samples_leaf=3, n_estimators=10........
[CV 3/5; 350/400] END max_depth=9, min_samples_leaf=3, n_estimators=10;, score=0.852 total time= 0.1s
[CV 4/5; 350/400] START max_depth=9, min_samples_leaf=3, n_estimators=10........
[CV 4/5; 350/400] END max_depth=9, min_samples_leaf=3, n_estimators=10;, score=0.855 total time= 0.1s
[CV 5/5; 350/400] START max_depth=9, min_samples_leaf=3, n_estimators=10........
[CV 5/5; 350/400] END max_depth=9, min_samples_leaf=3, n_estimators=10;, score=0.850 total time= 0.1s
[CV 1/5; 351/400] START max_depth=9, min_samples_leaf=4, n_estimators=1.........
[CV 1/5; 351/400] END max_depth=9, min_samples_leaf=4, n_estimators=1;, score=0.816 total time= 0.0s
[CV 2/5; 351/400] START max_depth=9, min_samples_leaf=4, n_estimators=1.........
[CV 2/5; 351/400] END max_depth=9, min_samples_leaf=4, n_estimators=1;, score=0.828 total time= 0.0s
[CV 3/5; 351/400] START max_depth=9, min_samples_leaf=4, n_estimators=1.........
[CV 3/5; 351/400] END max_depth=9, min_samples_leaf=4, n_estimators=1;, score=0.805 total time= 0.0s
[CV 4/5; 351/400] START max_depth=9, min_samples_leaf=4, n_estimators=1.........
[CV 4/5; 351/400] END max_depth=9, min_samples_leaf=4, n_estimators=1;, score=0.812 total time= 0.0s
[CV 5/5; 351/400] START max_depth=9, min_samples_leaf=4, n_estimators=1.........
[CV 5/5; 351/400] END max_depth=9, min_samples_leaf=4, n_estimators=1;, score=0.829 total time= 0.0s
[CV 1/5; 352/400] START max_depth=9, min_samples_leaf=4, n_estimators=2.........
[CV 1/5; 352/400] END max_depth=9, min_samples_leaf=4, n_estimators=2;, score=0.828 total time= 0.0s
[CV 2/5; 352/400] START max_depth=9, min_samples_leaf=4, n_estimators=2.........
[CV 2/5; 352/400] END max_depth=9, min_samples_leaf=4, n_estimators=2;, score=0.830 total time= 0.0s
[CV 3/5; 352/400] START max_depth=9, min_samples_leaf=4, n_estimators=2.........
[CV 3/5; 352/400] END max_depth=9, min_samples_leaf=4, n_estimators=2;, score=0.840 total time= 0.0s
[CV 4/5; 352/400] START max_depth=9, min_samples_leaf=4, n_estimators=2.........
[CV 4/5; 352/400] END max_depth=9, min_samples_leaf=4, n_estimators=2;, score=0.832 total time= 0.0s
[CV 5/5; 352/400] START max_depth=9, min_samples_leaf=4, n_estimators=2.........
[CV 5/5; 352/400] END max_depth=9, min_samples_leaf=4, n_estimators=2;, score=0.837 total time= 0.0s
[CV 1/5; 353/400] START max_depth=9, min_samples_leaf=4, n_estimators=3.........
[CV 1/5; 353/400] END max_depth=9, min_samples_leaf=4, n_estimators=3;, score=0.833 total time= 0.1s
[CV 2/5; 353/400] START max_depth=9, min_samples_leaf=4, n_estimators=3.........
[CV 2/5; 353/400] END max_depth=9, min_samples_leaf=4, n_estimators=3;, score=0.835 total time= 0.0s
[CV 3/5; 353/400] START max_depth=9, min_samples_leaf=4, n_estimators=3.........
[CV 3/5; 353/400] END max_depth=9, min_samples_leaf=4, n_estimators=3;, score=0.843 total time= 0.0s
[CV 4/5; 353/400] START max_depth=9, min_samples_leaf=4, n_estimators=3.........
[CV 4/5; 353/400] END max_depth=9, min_samples_leaf=4, n_estimators=3;, score=0.850 total time= 0.0s
[CV 5/5; 353/400] START max_depth=9, min_samples_leaf=4, n_estimators=3.........
[CV 5/5; 353/400] END max_depth=9, min_samples_leaf=4, n_estimators=3;, score=0.831 total time= 0.0s
[CV 1/5; 354/400] START max_depth=9, min_samples_leaf=4, n_estimators=4.........
[CV 1/5; 354/400] END max_depth=9, min_samples_leaf=4, n_estimators=4;, score=0.860 total time= 0.1s
[CV 2/5; 354/400] START max_depth=9, min_samples_leaf=4, n_estimators=4.........
[CV 2/5; 354/400] END max_depth=9, min_samples_leaf=4, n_estimators=4;, score=0.849 total time= 0.0s
[CV 3/5; 354/400] START max_depth=9, min_samples_leaf=4, n_estimators=4.........
[CV 3/5; 354/400] END max_depth=9, min_samples_leaf=4, n_estimators=4;, score=0.838 total time= 0.0s
[CV 4/5; 354/400] START max_depth=9, min_samples_leaf=4, n_estimators=4.........
[CV 4/5; 354/400] END max_depth=9, min_samples_leaf=4, n_estimators=4;, score=0.842 total time= 0.0s
[CV 5/5; 354/400] START max_depth=9, min_samples_leaf=4, n_estimators=4.........
[CV 5/5; 354/400] END max_depth=9, min_samples_leaf=4, n_estimators=4;, score=0.843 total time= 0.0s
[CV 1/5; 355/400] START max_depth=9, min_samples_leaf=4, n_estimators=5.........
[CV 1/5; 355/400] END max_depth=9, min_samples_leaf=4, n_estimators=5;, score=0.845 total time= 0.1s
[CV 2/5; 355/400] START max_depth=9, min_samples_leaf=4, n_estimators=5.........
[CV 2/5; 355/400] END max_depth=9, min_samples_leaf=4, n_estimators=5;, score=0.851 total time= 0.1s
[CV 3/5; 355/400] START max_depth=9, min_samples_leaf=4, n_estimators=5.........
[CV 3/5; 355/400] END max_depth=9, min_samples_leaf=4, n_estimators=5;, score=0.845 total time= 0.1s
[CV 4/5; 355/400] START max_depth=9, min_samples_leaf=4, n_estimators=5.........
[CV 4/5; 355/400] END max_depth=9, min_samples_leaf=4, n_estimators=5;, score=0.841 total time= 0.1s
[CV 5/5; 355/400] START max_depth=9, min_samples_leaf=4, n_estimators=5.........
[CV 5/5; 355/400] END max_depth=9, min_samples_leaf=4, n_estimators=5;, score=0.849 total time= 0.1s
[CV 1/5; 356/400] START max_depth=9, min_samples_leaf=4, n_estimators=6.........
[CV 1/5; 356/400] END max_depth=9, min_samples_leaf=4, n_estimators=6;, score=0.846 total time= 0.1s
[CV 2/5; 356/400] START max_depth=9, min_samples_leaf=4, n_estimators=6.........
[CV 2/5; 356/400] END max_depth=9, min_samples_leaf=4, n_estimators=6;, score=0.846 total time= 0.1s
[CV 3/5; 356/400] START max_depth=9, min_samples_leaf=4, n_estimators=6.........
[CV 3/5; 356/400] END max_depth=9, min_samples_leaf=4, n_estimators=6;, score=0.846 total time= 0.1s
[CV 4/5; 356/400] START max_depth=9, min_samples_leaf=4, n_estimators=6.........
[CV 4/5; 356/400] END max_depth=9, min_samples_leaf=4, n_estimators=6;, score=0.849 total time= 0.1s
[CV 5/5; 356/400] START max_depth=9, min_samples_leaf=4, n_estimators=6.........
[CV 5/5; 356/400] END max_depth=9, min_samples_leaf=4, n_estimators=6;, score=0.858 total time= 0.1s
[CV 1/5; 357/400] START max_depth=9, min_samples_leaf=4, n_estimators=7.........
[CV 1/5; 357/400] END max_depth=9, min_samples_leaf=4, n_estimators=7;, score=0.851 total time= 0.1s
[CV 2/5; 357/400] START max_depth=9, min_samples_leaf=4, n_estimators=7.........
[CV 2/5; 357/400] END max_depth=9, min_samples_leaf=4, n_estimators=7;, score=0.847 total time= 0.1s
[CV 3/5; 357/400] START max_depth=9, min_samples_leaf=4, n_estimators=7.........
[CV 3/5; 357/400] END max_depth=9, min_samples_leaf=4, n_estimators=7;, score=0.846 total time= 0.1s
[CV 4/5; 357/400] START max_depth=9, min_samples_leaf=4, n_estimators=7.........
[CV 4/5; 357/400] END max_depth=9, min_samples_leaf=4, n_estimators=7;, score=0.846 total time= 0.1s
[CV 5/5; 357/400] START max_depth=9, min_samples_leaf=4, n_estimators=7.........
[CV 5/5; 357/400] END max_depth=9, min_samples_leaf=4, n_estimators=7;, score=0.843 total time= 0.1s
[CV 1/5; 358/400] START max_depth=9, min_samples_leaf=4, n_estimators=8.........
[CV 1/5; 358/400] END max_depth=9, min_samples_leaf=4, n_estimators=8;, score=0.857 total time= 0.1s
[CV 2/5; 358/400] START max_depth=9, min_samples_leaf=4, n_estimators=8.........
[CV 2/5; 358/400] END max_depth=9, min_samples_leaf=4, n_estimators=8;, score=0.855 total time= 0.1s
[CV 3/5; 358/400] START max_depth=9, min_samples_leaf=4, n_estimators=8.........
[CV 3/5; 358/400] END max_depth=9, min_samples_leaf=4, n_estimators=8;, score=0.854 total time= 0.1s
[CV 4/5; 358/400] START max_depth=9, min_samples_leaf=4, n_estimators=8.........
[CV 4/5; 358/400] END max_depth=9, min_samples_leaf=4, n_estimators=8;, score=0.842 total time= 0.1s
[CV 5/5; 358/400] START max_depth=9, min_samples_leaf=4, n_estimators=8.........
[CV 5/5; 358/400] END max_depth=9, min_samples_leaf=4, n_estimators=8;, score=0.837 total time= 0.1s
[CV 1/5; 359/400] START max_depth=9, min_samples_leaf=4, n_estimators=9.........
[CV 1/5; 359/400] END max_depth=9, min_samples_leaf=4, n_estimators=9;, score=0.859 total time= 0.1s
[CV 2/5; 359/400] START max_depth=9, min_samples_leaf=4, n_estimators=9.........
[CV 2/5; 359/400] END max_depth=9, min_samples_leaf=4, n_estimators=9;, score=0.855 total time= 0.1s
[CV 3/5; 359/400] START max_depth=9, min_samples_leaf=4, n_estimators=9.........
[CV 3/5; 359/400] END max_depth=9, min_samples_leaf=4, n_estimators=9;, score=0.854 total time= 0.1s
[CV 4/5; 359/400] START max_depth=9, min_samples_leaf=4, n_estimators=9.........
[CV 4/5; 359/400] END max_depth=9, min_samples_leaf=4, n_estimators=9;, score=0.850 total time= 0.1s
[CV 5/5; 359/400] START max_depth=9, min_samples_leaf=4, n_estimators=9.........
[CV 5/5; 359/400] END max_depth=9, min_samples_leaf=4, n_estimators=9;, score=0.853 total time= 0.1s
[CV 1/5; 360/400] START max_depth=9, min_samples_leaf=4, n_estimators=10........
[CV 1/5; 360/400] END max_depth=9, min_samples_leaf=4, n_estimators=10;, score=0.843 total time= 0.1s
[CV 2/5; 360/400] START max_depth=9, min_samples_leaf=4, n_estimators=10........
[CV 2/5; 360/400] END max_depth=9, min_samples_leaf=4, n_estimators=10;, score=0.863 total time= 0.1s
[CV 3/5; 360/400] START max_depth=9, min_samples_leaf=4, n_estimators=10........
[CV 3/5; 360/400] END max_depth=9, min_samples_leaf=4, n_estimators=10;, score=0.845 total time= 0.1s
[CV 4/5; 360/400] START max_depth=9, min_samples_leaf=4, n_estimators=10........
[CV 4/5; 360/400] END max_depth=9, min_samples_leaf=4, n_estimators=10;, score=0.854 total time= 0.1s
[CV 5/5; 360/400] START max_depth=9, min_samples_leaf=4, n_estimators=10........
[CV 5/5; 360/400] END max_depth=9, min_samples_leaf=4, n_estimators=10;, score=0.857 total time= 0.1s
[CV 1/5; 361/400] START max_depth=10, min_samples_leaf=1, n_estimators=1........
[CV 1/5; 361/400] END max_depth=10, min_samples_leaf=1, n_estimators=1;, score=0.811 total time= 0.0s
[CV 2/5; 361/400] START max_depth=10, min_samples_leaf=1, n_estimators=1........
[CV 2/5; 361/400] END max_depth=10, min_samples_leaf=1, n_estimators=1;, score=0.813 total time= 0.0s
[CV 3/5; 361/400] START max_depth=10, min_samples_leaf=1, n_estimators=1........
[CV 3/5; 361/400] END max_depth=10, min_samples_leaf=1, n_estimators=1;, score=0.827 total time= 0.0s
[CV 4/5; 361/400] START max_depth=10, min_samples_leaf=1, n_estimators=1........
[CV 4/5; 361/400] END max_depth=10, min_samples_leaf=1, n_estimators=1;, score=0.819 total time= 0.0s
[CV 5/5; 361/400] START max_depth=10, min_samples_leaf=1, n_estimators=1........
[CV 5/5; 361/400] END max_depth=10, min_samples_leaf=1, n_estimators=1;, score=0.805 total time= 0.0s
[CV 1/5; 362/400] START max_depth=10, min_samples_leaf=1, n_estimators=2........
[CV 1/5; 362/400] END max_depth=10, min_samples_leaf=1, n_estimators=2;, score=0.859 total time= 0.0s
[CV 2/5; 362/400] START max_depth=10, min_samples_leaf=1, n_estimators=2........
[CV 2/5; 362/400] END max_depth=10, min_samples_leaf=1, n_estimators=2;, score=0.830 total time= 0.0s
[CV 3/5; 362/400] START max_depth=10, min_samples_leaf=1, n_estimators=2........
[CV 3/5; 362/400] END max_depth=10, min_samples_leaf=1, n_estimators=2;, score=0.849 total time= 0.0s
[CV 4/5; 362/400] START max_depth=10, min_samples_leaf=1, n_estimators=2........
[CV 4/5; 362/400] END max_depth=10, min_samples_leaf=1, n_estimators=2;, score=0.831 total time= 0.0s
[CV 5/5; 362/400] START max_depth=10, min_samples_leaf=1, n_estimators=2........
[CV 5/5; 362/400] END max_depth=10, min_samples_leaf=1, n_estimators=2;, score=0.866 total time= 0.0s
[CV 1/5; 363/400] START max_depth=10, min_samples_leaf=1, n_estimators=3........
[CV 1/5; 363/400] END max_depth=10, min_samples_leaf=1, n_estimators=3;, score=0.852 total time= 0.0s
[CV 2/5; 363/400] START max_depth=10, min_samples_leaf=1, n_estimators=3........
[CV 2/5; 363/400] END max_depth=10, min_samples_leaf=1, n_estimators=3;, score=0.838 total time= 0.0s
[CV 3/5; 363/400] START max_depth=10, min_samples_leaf=1, n_estimators=3........
[CV 3/5; 363/400] END max_depth=10, min_samples_leaf=1, n_estimators=3;, score=0.852 total time= 0.0s
[CV 4/5; 363/400] START max_depth=10, min_samples_leaf=1, n_estimators=3........
[CV 4/5; 363/400] END max_depth=10, min_samples_leaf=1, n_estimators=3;, score=0.847 total time= 0.0s
[CV 5/5; 363/400] START max_depth=10, min_samples_leaf=1, n_estimators=3........
[CV 5/5; 363/400] END max_depth=10, min_samples_leaf=1, n_estimators=3;, score=0.863 total time= 0.0s
[CV 1/5; 364/400] START max_depth=10, min_samples_leaf=1, n_estimators=4........
[CV 1/5; 364/400] END max_depth=10, min_samples_leaf=1, n_estimators=4;, score=0.846 total time= 0.0s
[CV 2/5; 364/400] START max_depth=10, min_samples_leaf=1, n_estimators=4........
[CV 2/5; 364/400] END max_depth=10, min_samples_leaf=1, n_estimators=4;, score=0.855 total time= 0.0s
[CV 3/5; 364/400] START max_depth=10, min_samples_leaf=1, n_estimators=4........
[CV 3/5; 364/400] END max_depth=10, min_samples_leaf=1, n_estimators=4;, score=0.868 total time= 0.0s
[CV 4/5; 364/400] START max_depth=10, min_samples_leaf=1, n_estimators=4........
[CV 4/5; 364/400] END max_depth=10, min_samples_leaf=1, n_estimators=4;, score=0.859 total time= 0.0s
[CV 5/5; 364/400] START max_depth=10, min_samples_leaf=1, n_estimators=4........
[CV 5/5; 364/400] END max_depth=10, min_samples_leaf=1, n_estimators=4;, score=0.860 total time= 0.0s
[CV 1/5; 365/400] START max_depth=10, min_samples_leaf=1, n_estimators=5........
[CV 1/5; 365/400] END max_depth=10, min_samples_leaf=1, n_estimators=5;, score=0.852 total time= 0.0s
[CV 2/5; 365/400] START max_depth=10, min_samples_leaf=1, n_estimators=5........
[CV 2/5; 365/400] END max_depth=10, min_samples_leaf=1, n_estimators=5;, score=0.867 total time= 0.0s
[CV 3/5; 365/400] START max_depth=10, min_samples_leaf=1, n_estimators=5........
[CV 3/5; 365/400] END max_depth=10, min_samples_leaf=1, n_estimators=5;, score=0.874 total time= 0.0s
[CV 4/5; 365/400] START max_depth=10, min_samples_leaf=1, n_estimators=5........
[CV 4/5; 365/400] END max_depth=10, min_samples_leaf=1, n_estimators=5;, score=0.852 total time= 0.0s
[CV 5/5; 365/400] START max_depth=10, min_samples_leaf=1, n_estimators=5........
[CV 5/5; 365/400] END max_depth=10, min_samples_leaf=1, n_estimators=5;, score=0.863 total time= 0.0s
[CV 1/5; 366/400] START max_depth=10, min_samples_leaf=1, n_estimators=6........
[CV 1/5; 366/400] END max_depth=10, min_samples_leaf=1, n_estimators=6;, score=0.871 total time= 0.1s
[CV 2/5; 366/400] START max_depth=10, min_samples_leaf=1, n_estimators=6........
[CV 2/5; 366/400] END max_depth=10, min_samples_leaf=1, n_estimators=6;, score=0.870 total time= 0.0s
[CV 3/5; 366/400] START max_depth=10, min_samples_leaf=1, n_estimators=6........
[CV 3/5; 366/400] END max_depth=10, min_samples_leaf=1, n_estimators=6;, score=0.870 total time= 0.0s
[CV 4/5; 366/400] START max_depth=10, min_samples_leaf=1, n_estimators=6........
[CV 4/5; 366/400] END max_depth=10, min_samples_leaf=1, n_estimators=6;, score=0.871 total time= 0.0s
[CV 5/5; 366/400] START max_depth=10, min_samples_leaf=1, n_estimators=6........
[CV 5/5; 366/400] END max_depth=10, min_samples_leaf=1, n_estimators=6;, score=0.855 total time= 0.1s
[CV 1/5; 367/400] START max_depth=10, min_samples_leaf=1, n_estimators=7........
[CV 1/5; 367/400] END max_depth=10, min_samples_leaf=1, n_estimators=7;, score=0.867 total time= 0.1s
[CV 2/5; 367/400] START max_depth=10, min_samples_leaf=1, n_estimators=7........
[CV 2/5; 367/400] END max_depth=10, min_samples_leaf=1, n_estimators=7;, score=0.878 total time= 0.1s
[CV 3/5; 367/400] START max_depth=10, min_samples_leaf=1, n_estimators=7........
[CV 3/5; 367/400] END max_depth=10, min_samples_leaf=1, n_estimators=7;, score=0.871 total time= 0.1s
[CV 4/5; 367/400] START max_depth=10, min_samples_leaf=1, n_estimators=7........
[CV 4/5; 367/400] END max_depth=10, min_samples_leaf=1, n_estimators=7;, score=0.862 total time= 0.1s
[CV 5/5; 367/400] START max_depth=10, min_samples_leaf=1, n_estimators=7........
[CV 5/5; 367/400] END max_depth=10, min_samples_leaf=1, n_estimators=7;, score=0.863 total time= 0.1s
[CV 1/5; 368/400] START max_depth=10, min_samples_leaf=1, n_estimators=8........
[CV 1/5; 368/400] END max_depth=10, min_samples_leaf=1, n_estimators=8;, score=0.876 total time= 0.1s
[CV 2/5; 368/400] START max_depth=10, min_samples_leaf=1, n_estimators=8........
[CV 2/5; 368/400] END max_depth=10, min_samples_leaf=1, n_estimators=8;, score=0.866 total time= 0.1s
[CV 3/5; 368/400] START max_depth=10, min_samples_leaf=1, n_estimators=8........
[CV 3/5; 368/400] END max_depth=10, min_samples_leaf=1, n_estimators=8;, score=0.871 total time= 0.1s
[CV 4/5; 368/400] START max_depth=10, min_samples_leaf=1, n_estimators=8........
[CV 4/5; 368/400] END max_depth=10, min_samples_leaf=1, n_estimators=8;, score=0.876 total time= 0.1s
[CV 5/5; 368/400] START max_depth=10, min_samples_leaf=1, n_estimators=8........
[CV 5/5; 368/400] END max_depth=10, min_samples_leaf=1, n_estimators=8;, score=0.877 total time= 0.1s
[CV 1/5; 369/400] START max_depth=10, min_samples_leaf=1, n_estimators=9........
[CV 1/5; 369/400] END max_depth=10, min_samples_leaf=1, n_estimators=9;, score=0.875 total time= 0.1s
[CV 2/5; 369/400] START max_depth=10, min_samples_leaf=1, n_estimators=9........
[CV 2/5; 369/400] END max_depth=10, min_samples_leaf=1, n_estimators=9;, score=0.866 total time= 0.1s
[CV 3/5; 369/400] START max_depth=10, min_samples_leaf=1, n_estimators=9........
[CV 3/5; 369/400] END max_depth=10, min_samples_leaf=1, n_estimators=9;, score=0.860 total time= 0.1s
[CV 4/5; 369/400] START max_depth=10, min_samples_leaf=1, n_estimators=9........
[CV 4/5; 369/400] END max_depth=10, min_samples_leaf=1, n_estimators=9;, score=0.862 total time= 0.1s
[CV 5/5; 369/400] START max_depth=10, min_samples_leaf=1, n_estimators=9........
[CV 5/5; 369/400] END max_depth=10, min_samples_leaf=1, n_estimators=9;, score=0.873 total time= 0.1s
[CV 1/5; 370/400] START max_depth=10, min_samples_leaf=1, n_estimators=10.......
[CV 1/5; 370/400] END max_depth=10, min_samples_leaf=1, n_estimators=10;, score=0.877 total time= 0.1s
[CV 2/5; 370/400] START max_depth=10, min_samples_leaf=1, n_estimators=10.......
[CV 2/5; 370/400] END max_depth=10, min_samples_leaf=1, n_estimators=10;, score=0.866 total time= 0.1s
[CV 3/5; 370/400] START max_depth=10, min_samples_leaf=1, n_estimators=10.......
[CV 3/5; 370/400] END max_depth=10, min_samples_leaf=1, n_estimators=10;, score=0.874 total time= 0.1s
[CV 4/5; 370/400] START max_depth=10, min_samples_leaf=1, n_estimators=10.......
[CV 4/5; 370/400] END max_depth=10, min_samples_leaf=1, n_estimators=10;, score=0.874 total time= 0.1s
[CV 5/5; 370/400] START max_depth=10, min_samples_leaf=1, n_estimators=10.......
[CV 5/5; 370/400] END max_depth=10, min_samples_leaf=1, n_estimators=10;, score=0.871 total time= 0.1s
[CV 1/5; 371/400] START max_depth=10, min_samples_leaf=2, n_estimators=1........
[CV 1/5; 371/400] END max_depth=10, min_samples_leaf=2, n_estimators=1;, score=0.802 total time= 0.0s
[CV 2/5; 371/400] START max_depth=10, min_samples_leaf=2, n_estimators=1........
[CV 2/5; 371/400] END max_depth=10, min_samples_leaf=2, n_estimators=1;, score=0.811 total time= 0.0s
[CV 3/5; 371/400] START max_depth=10, min_samples_leaf=2, n_estimators=1........
[CV 3/5; 371/400] END max_depth=10, min_samples_leaf=2, n_estimators=1;, score=0.824 total time= 0.0s
[CV 4/5; 371/400] START max_depth=10, min_samples_leaf=2, n_estimators=1........
[CV 4/5; 371/400] END max_depth=10, min_samples_leaf=2, n_estimators=1;, score=0.818 total time= 0.0s
[CV 5/5; 371/400] START max_depth=10, min_samples_leaf=2, n_estimators=1........
[CV 5/5; 371/400] END max_depth=10, min_samples_leaf=2, n_estimators=1;, score=0.837 total time= 0.0s
[CV 1/5; 372/400] START max_depth=10, min_samples_leaf=2, n_estimators=2........
[CV 1/5; 372/400] END max_depth=10, min_samples_leaf=2, n_estimators=2;, score=0.850 total time= 0.0s
[CV 2/5; 372/400] START max_depth=10, min_samples_leaf=2, n_estimators=2........
[CV 2/5; 372/400] END max_depth=10, min_samples_leaf=2, n_estimators=2;, score=0.825 total time= 0.0s
[CV 3/5; 372/400] START max_depth=10, min_samples_leaf=2, n_estimators=2........
[CV 3/5; 372/400] END max_depth=10, min_samples_leaf=2, n_estimators=2;, score=0.842 total time= 0.0s
[CV 4/5; 372/400] START max_depth=10, min_samples_leaf=2, n_estimators=2........
[CV 4/5; 372/400] END max_depth=10, min_samples_leaf=2, n_estimators=2;, score=0.833 total time= 0.0s
[CV 5/5; 372/400] START max_depth=10, min_samples_leaf=2, n_estimators=2........
[CV 5/5; 372/400] END max_depth=10, min_samples_leaf=2, n_estimators=2;, score=0.843 total time= 0.0s
[CV 1/5; 373/400] START max_depth=10, min_samples_leaf=2, n_estimators=3........
[CV 1/5; 373/400] END max_depth=10, min_samples_leaf=2, n_estimators=3;, score=0.858 total time= 0.0s
[CV 2/5; 373/400] START max_depth=10, min_samples_leaf=2, n_estimators=3........
[CV 2/5; 373/400] END max_depth=10, min_samples_leaf=2, n_estimators=3;, score=0.843 total time= 0.0s
[CV 3/5; 373/400] START max_depth=10, min_samples_leaf=2, n_estimators=3........
[CV 3/5; 373/400] END max_depth=10, min_samples_leaf=2, n_estimators=3;, score=0.853 total time= 0.0s
[CV 4/5; 373/400] START max_depth=10, min_samples_leaf=2, n_estimators=3........
[CV 4/5; 373/400] END max_depth=10, min_samples_leaf=2, n_estimators=3;, score=0.843 total time= 0.0s
[CV 5/5; 373/400] START max_depth=10, min_samples_leaf=2, n_estimators=3........
[CV 5/5; 373/400] END max_depth=10, min_samples_leaf=2, n_estimators=3;, score=0.825 total time= 0.0s
[CV 1/5; 374/400] START max_depth=10, min_samples_leaf=2, n_estimators=4........
[CV 1/5; 374/400] END max_depth=10, min_samples_leaf=2, n_estimators=4;, score=0.856 total time= 0.0s
[CV 2/5; 374/400] START max_depth=10, min_samples_leaf=2, n_estimators=4........
[CV 2/5; 374/400] END max_depth=10, min_samples_leaf=2, n_estimators=4;, score=0.854 total time= 0.0s
[CV 3/5; 374/400] START max_depth=10, min_samples_leaf=2, n_estimators=4........
[CV 3/5; 374/400] END max_depth=10, min_samples_leaf=2, n_estimators=4;, score=0.860 total time= 0.0s
[CV 4/5; 374/400] START max_depth=10, min_samples_leaf=2, n_estimators=4........
[CV 4/5; 374/400] END max_depth=10, min_samples_leaf=2, n_estimators=4;, score=0.853 total time= 0.0s
[CV 5/5; 374/400] START max_depth=10, min_samples_leaf=2, n_estimators=4........
[CV 5/5; 374/400] END max_depth=10, min_samples_leaf=2, n_estimators=4;, score=0.874 total time= 0.0s
[CV 1/5; 375/400] START max_depth=10, min_samples_leaf=2, n_estimators=5........
[CV 1/5; 375/400] END max_depth=10, min_samples_leaf=2, n_estimators=5;, score=0.864 total time= 0.0s
[CV 2/5; 375/400] START max_depth=10, min_samples_leaf=2, n_estimators=5........
[CV 2/5; 375/400] END max_depth=10, min_samples_leaf=2, n_estimators=5;, score=0.865 total time= 0.0s
[CV 3/5; 375/400] START max_depth=10, min_samples_leaf=2, n_estimators=5........
[CV 3/5; 375/400] END max_depth=10, min_samples_leaf=2, n_estimators=5;, score=0.861 total time= 0.0s
[CV 4/5; 375/400] START max_depth=10, min_samples_leaf=2, n_estimators=5........
[CV 4/5; 375/400] END max_depth=10, min_samples_leaf=2, n_estimators=5;, score=0.865 total time= 0.0s
[CV 5/5; 375/400] START max_depth=10, min_samples_leaf=2, n_estimators=5........
[CV 5/5; 375/400] END max_depth=10, min_samples_leaf=2, n_estimators=5;, score=0.856 total time= 0.0s
[CV 1/5; 376/400] START max_depth=10, min_samples_leaf=2, n_estimators=6........
[CV 1/5; 376/400] END max_depth=10, min_samples_leaf=2, n_estimators=6;, score=0.863 total time= 0.0s
[CV 2/5; 376/400] START max_depth=10, min_samples_leaf=2, n_estimators=6........
[CV 2/5; 376/400] END max_depth=10, min_samples_leaf=2, n_estimators=6;, score=0.860 total time= 0.0s
[CV 3/5; 376/400] START max_depth=10, min_samples_leaf=2, n_estimators=6........
[CV 3/5; 376/400] END max_depth=10, min_samples_leaf=2, n_estimators=6;, score=0.860 total time= 0.1s
[CV 4/5; 376/400] START max_depth=10, min_samples_leaf=2, n_estimators=6........
[CV 4/5; 376/400] END max_depth=10, min_samples_leaf=2, n_estimators=6;, score=0.863 total time= 0.1s
[CV 5/5; 376/400] START max_depth=10, min_samples_leaf=2, n_estimators=6........
[CV 5/5; 376/400] END max_depth=10, min_samples_leaf=2, n_estimators=6;, score=0.870 total time= 0.1s
[CV 1/5; 377/400] START max_depth=10, min_samples_leaf=2, n_estimators=7........
[CV 1/5; 377/400] END max_depth=10, min_samples_leaf=2, n_estimators=7;, score=0.876 total time= 0.1s
[CV 2/5; 377/400] START max_depth=10, min_samples_leaf=2, n_estimators=7........
[CV 2/5; 377/400] END max_depth=10, min_samples_leaf=2, n_estimators=7;, score=0.864 total time= 0.1s
[CV 3/5; 377/400] START max_depth=10, min_samples_leaf=2, n_estimators=7........
[CV 3/5; 377/400] END max_depth=10, min_samples_leaf=2, n_estimators=7;, score=0.874 total time= 0.1s
[CV 4/5; 377/400] START max_depth=10, min_samples_leaf=2, n_estimators=7........
[CV 4/5; 377/400] END max_depth=10, min_samples_leaf=2, n_estimators=7;, score=0.871 total time= 0.1s
[CV 5/5; 377/400] START max_depth=10, min_samples_leaf=2, n_estimators=7........
[CV 5/5; 377/400] END max_depth=10, min_samples_leaf=2, n_estimators=7;, score=0.860 total time= 0.1s
[CV 1/5; 378/400] START max_depth=10, min_samples_leaf=2, n_estimators=8........
[CV 1/5; 378/400] END max_depth=10, min_samples_leaf=2, n_estimators=8;, score=0.874 total time= 0.1s
[CV 2/5; 378/400] START max_depth=10, min_samples_leaf=2, n_estimators=8........
[CV 2/5; 378/400] END max_depth=10, min_samples_leaf=2, n_estimators=8;, score=0.861 total time= 0.1s
[CV 3/5; 378/400] START max_depth=10, min_samples_leaf=2, n_estimators=8........
[CV 3/5; 378/400] END max_depth=10, min_samples_leaf=2, n_estimators=8;, score=0.864 total time= 0.1s
[CV 4/5; 378/400] START max_depth=10, min_samples_leaf=2, n_estimators=8........
[CV 4/5; 378/400] END max_depth=10, min_samples_leaf=2, n_estimators=8;, score=0.867 total time= 0.1s
[CV 5/5; 378/400] START max_depth=10, min_samples_leaf=2, n_estimators=8........
[CV 5/5; 378/400] END max_depth=10, min_samples_leaf=2, n_estimators=8;, score=0.870 total time= 0.1s
[CV 1/5; 379/400] START max_depth=10, min_samples_leaf=2, n_estimators=9........
[CV 1/5; 379/400] END max_depth=10, min_samples_leaf=2, n_estimators=9;, score=0.869 total time= 0.1s
[CV 2/5; 379/400] START max_depth=10, min_samples_leaf=2, n_estimators=9........
[CV 2/5; 379/400] END max_depth=10, min_samples_leaf=2, n_estimators=9;, score=0.871 total time= 0.1s
[CV 3/5; 379/400] START max_depth=10, min_samples_leaf=2, n_estimators=9........
[CV 3/5; 379/400] END max_depth=10, min_samples_leaf=2, n_estimators=9;, score=0.877 total time= 0.1s
[CV 4/5; 379/400] START max_depth=10, min_samples_leaf=2, n_estimators=9........
[CV 4/5; 379/400] END max_depth=10, min_samples_leaf=2, n_estimators=9;, score=0.862 total time= 0.1s
[CV 5/5; 379/400] START max_depth=10, min_samples_leaf=2, n_estimators=9........
[CV 5/5; 379/400] END max_depth=10, min_samples_leaf=2, n_estimators=9;, score=0.864 total time= 0.1s
[CV 1/5; 380/400] START max_depth=10, min_samples_leaf=2, n_estimators=10.......
[CV 1/5; 380/400] END max_depth=10, min_samples_leaf=2, n_estimators=10;, score=0.864 total time= 0.1s
[CV 2/5; 380/400] START max_depth=10, min_samples_leaf=2, n_estimators=10.......
[CV 2/5; 380/400] END max_depth=10, min_samples_leaf=2, n_estimators=10;, score=0.874 total time= 0.1s
[CV 3/5; 380/400] START max_depth=10, min_samples_leaf=2, n_estimators=10.......
[CV 3/5; 380/400] END max_depth=10, min_samples_leaf=2, n_estimators=10;, score=0.870 total time= 0.1s
[CV 4/5; 380/400] START max_depth=10, min_samples_leaf=2, n_estimators=10.......
[CV 4/5; 380/400] END max_depth=10, min_samples_leaf=2, n_estimators=10;, score=0.875 total time= 0.1s
[CV 5/5; 380/400] START max_depth=10, min_samples_leaf=2, n_estimators=10.......
[CV 5/5; 380/400] END max_depth=10, min_samples_leaf=2, n_estimators=10;, score=0.863 total time= 0.1s
[CV 1/5; 381/400] START max_depth=10, min_samples_leaf=3, n_estimators=1........
[CV 1/5; 381/400] END max_depth=10, min_samples_leaf=3, n_estimators=1;, score=0.808 total time= 0.0s
[CV 2/5; 381/400] START max_depth=10, min_samples_leaf=3, n_estimators=1........
[CV 2/5; 381/400] END max_depth=10, min_samples_leaf=3, n_estimators=1;, score=0.819 total time= 0.0s
[CV 3/5; 381/400] START max_depth=10, min_samples_leaf=3, n_estimators=1........
[CV 3/5; 381/400] END max_depth=10, min_samples_leaf=3, n_estimators=1;, score=0.825 total time= 0.0s
[CV 4/5; 381/400] START max_depth=10, min_samples_leaf=3, n_estimators=1........
[CV 4/5; 381/400] END max_depth=10, min_samples_leaf=3, n_estimators=1;, score=0.794 total time= 0.0s
[CV 5/5; 381/400] START max_depth=10, min_samples_leaf=3, n_estimators=1........
[CV 5/5; 381/400] END max_depth=10, min_samples_leaf=3, n_estimators=1;, score=0.810 total time= 0.0s
[CV 1/5; 382/400] START max_depth=10, min_samples_leaf=3, n_estimators=2........
[CV 1/5; 382/400] END max_depth=10, min_samples_leaf=3, n_estimators=2;, score=0.850 total time= 0.0s
[CV 2/5; 382/400] START max_depth=10, min_samples_leaf=3, n_estimators=2........
[CV 2/5; 382/400] END max_depth=10, min_samples_leaf=3, n_estimators=2;, score=0.821 total time= 0.0s
[CV 3/5; 382/400] START max_depth=10, min_samples_leaf=3, n_estimators=2........
[CV 3/5; 382/400] END max_depth=10, min_samples_leaf=3, n_estimators=2;, score=0.833 total time= 0.0s
[CV 4/5; 382/400] START max_depth=10, min_samples_leaf=3, n_estimators=2........
[CV 4/5; 382/400] END max_depth=10, min_samples_leaf=3, n_estimators=2;, score=0.838 total time= 0.0s
[CV 5/5; 382/400] START max_depth=10, min_samples_leaf=3, n_estimators=2........
[CV 5/5; 382/400] END max_depth=10, min_samples_leaf=3, n_estimators=2;, score=0.829 total time= 0.0s
[CV 1/5; 383/400] START max_depth=10, min_samples_leaf=3, n_estimators=3........
[CV 1/5; 383/400] END max_depth=10, min_samples_leaf=3, n_estimators=3;, score=0.858 total time= 0.0s
[CV 2/5; 383/400] START max_depth=10, min_samples_leaf=3, n_estimators=3........
[CV 2/5; 383/400] END max_depth=10, min_samples_leaf=3, n_estimators=3;, score=0.848 total time= 0.0s
[CV 3/5; 383/400] START max_depth=10, min_samples_leaf=3, n_estimators=3........
[CV 3/5; 383/400] END max_depth=10, min_samples_leaf=3, n_estimators=3;, score=0.838 total time= 0.0s
[CV 4/5; 383/400] START max_depth=10, min_samples_leaf=3, n_estimators=3........
[CV 4/5; 383/400] END max_depth=10, min_samples_leaf=3, n_estimators=3;, score=0.851 total time= 0.0s
[CV 5/5; 383/400] START max_depth=10, min_samples_leaf=3, n_estimators=3........
[CV 5/5; 383/400] END max_depth=10, min_samples_leaf=3, n_estimators=3;, score=0.860 total time= 0.0s
[CV 1/5; 384/400] START max_depth=10, min_samples_leaf=3, n_estimators=4........
[CV 1/5; 384/400] END max_depth=10, min_samples_leaf=3, n_estimators=4;, score=0.863 total time= 0.0s
[CV 2/5; 384/400] START max_depth=10, min_samples_leaf=3, n_estimators=4........
[CV 2/5; 384/400] END max_depth=10, min_samples_leaf=3, n_estimators=4;, score=0.855 total time= 0.0s
[CV 3/5; 384/400] START max_depth=10, min_samples_leaf=3, n_estimators=4........
[CV 3/5; 384/400] END max_depth=10, min_samples_leaf=3, n_estimators=4;, score=0.861 total time= 0.0s
[CV 4/5; 384/400] START max_depth=10, min_samples_leaf=3, n_estimators=4........
[CV 4/5; 384/400] END max_depth=10, min_samples_leaf=3, n_estimators=4;, score=0.852 total time= 0.0s
[CV 5/5; 384/400] START max_depth=10, min_samples_leaf=3, n_estimators=4........
[CV 5/5; 384/400] END max_depth=10, min_samples_leaf=3, n_estimators=4;, score=0.871 total time= 0.0s
[CV 1/5; 385/400] START max_depth=10, min_samples_leaf=3, n_estimators=5........
[CV 1/5; 385/400] END max_depth=10, min_samples_leaf=3, n_estimators=5;, score=0.857 total time= 0.0s
[CV 2/5; 385/400] START max_depth=10, min_samples_leaf=3, n_estimators=5........
[CV 2/5; 385/400] END max_depth=10, min_samples_leaf=3, n_estimators=5;, score=0.846 total time= 0.0s
[CV 3/5; 385/400] START max_depth=10, min_samples_leaf=3, n_estimators=5........
[CV 3/5; 385/400] END max_depth=10, min_samples_leaf=3, n_estimators=5;, score=0.844 total time= 0.0s
[CV 4/5; 385/400] START max_depth=10, min_samples_leaf=3, n_estimators=5........
[CV 4/5; 385/400] END max_depth=10, min_samples_leaf=3, n_estimators=5;, score=0.866 total time= 0.0s
[CV 5/5; 385/400] START max_depth=10, min_samples_leaf=3, n_estimators=5........
[CV 5/5; 385/400] END max_depth=10, min_samples_leaf=3, n_estimators=5;, score=0.862 total time= 0.1s
[CV 1/5; 386/400] START max_depth=10, min_samples_leaf=3, n_estimators=6........
[CV 1/5; 386/400] END max_depth=10, min_samples_leaf=3, n_estimators=6;, score=0.876 total time= 0.0s
[CV 2/5; 386/400] START max_depth=10, min_samples_leaf=3, n_estimators=6........
[CV 2/5; 386/400] END max_depth=10, min_samples_leaf=3, n_estimators=6;, score=0.855 total time= 0.0s
[CV 3/5; 386/400] START max_depth=10, min_samples_leaf=3, n_estimators=6........
[CV 3/5; 386/400] END max_depth=10, min_samples_leaf=3, n_estimators=6;, score=0.863 total time= 0.0s
[CV 4/5; 386/400] START max_depth=10, min_samples_leaf=3, n_estimators=6........
[CV 4/5; 386/400] END max_depth=10, min_samples_leaf=3, n_estimators=6;, score=0.864 total time= 0.1s
[CV 5/5; 386/400] START max_depth=10, min_samples_leaf=3, n_estimators=6........
[CV 5/5; 386/400] END max_depth=10, min_samples_leaf=3, n_estimators=6;, score=0.855 total time= 0.0s
[CV 1/5; 387/400] START max_depth=10, min_samples_leaf=3, n_estimators=7........
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VOILA LES MEILLEURS PARAMÉTRES : {'max_depth': 10, 'min_samples_leaf': 1, 'n_estimators': 8}
Out[ ]:
RandomForestClassifier(max_depth=10, n_estimators=8)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
RandomForestClassifier(max_depth=10, n_estimators=8)
Prédiction¶
In [ ]:
# Prédiction sur la base test
best_Y_test_pred_RF = best_modele_RF.predict(X_test)
best_Y_test_pred_RF
Out[ ]:
array([1, 0, 0, ..., 0, 0, 1])
In [ ]:
# Prédiction sur la base train
best_Y_train_pred_RF = best_modele_RF.predict(X_train)
best_Y_train_pred_RF
Out[ ]:
array([1, 0, 1, ..., 1, 0, 1])
In [ ]:
# prediction en proba sur la base train
Y_pred_train_proba_RF = best_modele_RF.predict_proba(X_train)
Y_pred_train_proba_RF
Out[ ]:
array([[0.1396048 , 0.8603952 ],
[0.52972976, 0.47027024],
[0.17221296, 0.82778704],
...,
[0.20066609, 0.79933391],
[0.99781386, 0.00218614],
[0.24464578, 0.75535422]])
Mesures de performance¶
Accuracy¶
In [ ]:
# Sur la base test
Acc_test_RF = accuracy_score(Y_test, best_Y_test_pred_RF) * 100
Acc_train_RF = accuracy_score(Y_train, best_Y_train_pred_RF) * 100
# Sur la base train
print("Accuracy sur train:", Acc_train_RF, "%")
print("Accuracy sur test:", Acc_test_RF, "%")
Accuracy sur train: 90.58045554739162 % Accuracy sur test: 87.00719917723688 %
F1 score¶
In [ ]:
# Sur la base test et train
F1_score_test_RF = f1_score(Y_test, best_Y_test_pred_RF, average='weighted') * 100
F1_score_train_RF = f1_score(Y_train, best_Y_train_pred_RF, average='weighted') * 100
# Sur la base train
print("F1_score sur train:", F1_score_train_RF, "%")
print("F1_score sur test:", F1_score_test_RF, "%")
F1_score sur train: 90.54154506034475 % F1_score sur test: 86.95568107455549 %
Précision¶
In [ ]:
# Sur la base test et train
precision_score_test_RF = precision_score(Y_test, best_Y_test_pred_RF, average='weighted') * 100
precision_score_train_RF = precision_score(Y_train, best_Y_train_pred_RF, average='weighted') * 100
# Sur la base train
print("Précision sur train:", precision_score_train_RF, "%")
print("Précision sur test:", precision_score_test_RF, "%")
Précision sur train: 91.23370081323257 % Précision sur test: 87.66095562937005 %
Recall¶
In [ ]:
# Sur la base test et train
recall_score_test_RF = recall_score(Y_test, best_Y_test_pred_RF, average='weighted') * 100
recall_score_train_RF = recall_score(Y_train, best_Y_train_pred_RF, average='weighted') * 100
# Sur la base train
print("Recall sur train:", recall_score_train_RF, "%")
print("Recall sur test:", recall_score_test_RF, "%")
Recall sur train: 90.58045554739162 % Recall sur test: 87.00719917723688 %
Matrice de confusion¶
In [ ]:
matrice_RF = confusion_matrix(Y_test, best_Y_test_pred_RF)
# Affichage de la matrice de confusion avec titre
fig, ax = plot_confusion_matrix(conf_mat=matrice_RF,
show_absolute=True,
show_normed=True,
colorbar=True)
plt.title("Confusion Matrix RF")
plt.show()
print("Alors dans la classe 0,sur",Y_test.value_counts()[0],
"individu,le modèle réussit à faire un bon classement sur", matrice_RF[0,0],
" individu et une erreur sur", matrice_RF[0,1], "\n Dans la classe 1,sur",Y_test.value_counts()[1],
"individus le modèle fait un bon classement sur", matrice_RF[1,1],
" individu et une erreur sur", matrice_RF[0,0] )
Alors dans la classe 0,sur 1466 individu,le modèle réussit à faire un bon classement sur 1181 individu et une erreur sur 285 Dans la classe 1,sur 1451 individus le modèle fait un bon classement sur 1357 individu et une erreur sur 1181
*taux de bon et de mauvais classement*
In [ ]:
print(f"Taux de bon classement (Classe 0 - Sans AVC): {matrice_RF[0, 0] / Y_test.value_counts()[0] * 100:.2f} %")
print(f"Taux de mauvais classement (Classe 0 - Sans AVC): {matrice_RF[0, 1] / Y_test.value_counts()[0] * 100:.2f} %")
print(f"Taux de bon classement (Classe 1 - Avec AVC): {matrice_RF[1, 1] / Y_test.value_counts()[1] * 100:.2f} %")
print(f"Taux de mauvais classement (Classe 1 - Avec AVC): {matrice_RF[1, 0] / Y_test.value_counts()[1] * 100:.2f} %")
Taux de bon classement (Classe 0 - Sans AVC): 80.56 % Taux de mauvais classement (Classe 0 - Sans AVC): 19.44 % Taux de bon classement (Classe 1 - Avec AVC): 93.52 % Taux de mauvais classement (Classe 1 - Avec AVC): 6.48 %
In [ ]:
# Courbe ROC
fpr_RF, tpr_RF, thresholds_RF = roc_curve(Y_test, best_modele_RF.predict_proba(X_test)[:, 1])
roc_auc_RF = auc(fpr_RF, tpr_RF)
plt.figure()
plt.plot(fpr_RF, tpr_RF, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc_RF)
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('Taux de faux positif')
plt.ylabel('Taux de vrai positif')
plt.title('Courbe de ROC pour Random forest')
plt.legend(loc="lower right")
plt.show()
In [ ]:
result = permutation_importance(best_modele_RF, X, Y, n_repeats=10)
sorted_idx = result.importances_mean.argsort()
plt.barh(X.columns[sorted_idx], result.importances_mean[sorted_idx])
plt.xlabel("Importance des caractéristiques")
plt.show()
SupportVectorMachine(SVM)¶
Optimisation des paramètres¶
In [ ]:
# initialisation du modele
modele_SVC = SVC(probability=True)
# optimisation des parametres
param_grid_SVC = ({'C' : [0.1, 1, 10, 100],
'kernel' : ['rbf'],
'gamma' : ['scale', 0.01, 0.1, 1]})
# modele optimal
modele_opt_SVC = GridSearchCV(modele_SVC, param_grid_SVC,
cv = 5
)
## Entrainement du modèle
modele_opt_SVC.fit(X_train,Y_train)
# parametre optimaux
best_param = modele_opt_SVC.best_params_
print("VOILA LES MEILLEURS PARAMÉTRES :",best_param)
# best modele
best_modele_SVC = modele_opt_SVC.best_estimator_
best_modele_SVC
VOILA LES MEILLEURS PARAMÉTRES : {'C': 100, 'gamma': 1, 'kernel': 'rbf'}
Out[ ]:
SVC(C=100, gamma=1, probability=True)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
SVC(C=100, gamma=1, probability=True)
Prédiction¶
In [ ]:
# Prédiction sur la base test
best_Y_test_pred_SVC = best_modele_SVC.predict(X_test)
best_Y_test_pred_SVC
Out[ ]:
array([1, 0, 0, ..., 0, 0, 0])
In [ ]:
# Prédiction sur la base train
best_Y_train_pred_SVC = best_modele_SVC.predict(X_train)
best_Y_train_pred_SVC
Out[ ]:
array([1, 1, 0, ..., 1, 0, 1])
In [ ]:
# prediction en proba sur la base train
Y_pred_train_proba_SVC = best_modele_SVC.predict_proba(X_train)
Y_pred_train_proba_SVC
Out[ ]:
array([[8.67771555e-02, 9.13222845e-01],
[1.76050770e-01, 8.23949230e-01],
[8.96086596e-01, 1.03913404e-01],
...,
[1.26854662e-01, 8.73145338e-01],
[9.99648435e-01, 3.51564740e-04],
[1.32489438e-01, 8.67510562e-01]])
Mesures de performance¶
Accuracy¶
In [ ]:
# Sur la base test
Acc_test_SVC = accuracy_score(Y_test, best_Y_test_pred_SVC) * 100
Acc_train_SVC = accuracy_score(Y_train, best_Y_train_pred_SVC) * 100
# Sur la base train
print("Accuracy sur train:", Acc_train_SVC, "%")
print("Accuracy sur test:", Acc_test_SVC, "%")
Accuracy sur train: 96.57604702424688 % Accuracy sur test: 91.7723688721289 %
F1 score¶
In [ ]:
# Sur la base test et train
F1_score_test_SVC = f1_score(Y_test, best_Y_test_pred_SVC, average='weighted') * 100
F1_score_train_SVC = f1_score(Y_train, best_Y_train_pred_SVC, average='weighted') * 100
# Sur la base train
print("F1_score sur train:", F1_score_train_SVC, "%")
print("F1_score sur test:", F1_score_test_SVC, "%")
F1_score sur train: 96.57400009921112 % F1_score sur test: 91.76016955331053 %
Précision¶
In [ ]:
# Sur la base test et train
precision_score_test_SVC = precision_score(Y_test, best_Y_test_pred_SVC, average='weighted') * 100
precision_score_train_SVC = precision_score(Y_train, best_Y_train_pred_SVC, average='weighted') * 100
# Sur la base train
print("Précision sur train:", precision_score_train_SVC, "%")
print("Précision sur test:", precision_score_test_SVC, "%")
Précision sur train: 96.67764691398 % Précision sur test: 92.06033670088134 %
Recall¶
In [ ]:
# Sur la base test et train
recall_score_test_SVC = recall_score(Y_test, best_Y_test_pred_SVC, average='weighted') * 100
recall_score_train_SVC = recall_score(Y_train, best_Y_train_pred_SVC, average='weighted') * 100
# Sur la base train
print("Recall sur train:", recall_score_train_SVC, "%")
print("Recall sur test:", recall_score_test_SVC, "%")
Recall sur train: 96.57604702424688 % Recall sur test: 91.7723688721289 %
Matrice de confusion¶
In [ ]:
matrice_SVM = confusion_matrix(Y_test, best_Y_test_pred_SVC)
# Affichage de la matrice de confusion avec titre
fig, ax = plot_confusion_matrix(conf_mat=matrice_SVM,
show_absolute=True,
show_normed=True,
colorbar=True)
plt.title("Confusion Matrix SVM")
plt.show()
print("Alors dans la classe 0,sur",Y_test.value_counts()[0],
"individu,le modèle réussit à faire un bon classement sur", matrice_SVM[0,0],
" individu et une erreur sur", matrice_SVM[0,1], "\n Dans la classe 1,sur",Y_test.value_counts()[1],
"individus le modèle fait un bon classement sur", matrice_SVM[1,1],
" individu et une erreur sur", matrice_SVM[0,0] )
Alors dans la classe 0,sur 1466 individu,le modèle réussit à faire un bon classement sur 1286 individu et une erreur sur 180 Dans la classe 1,sur 1451 individus le modèle fait un bon classement sur 1391 individu et une erreur sur 1286
*taux de bon et de mauvais classement*
In [ ]:
print(f"Taux de bon classement (Classe 0 - Sans AVC): {matrice_SVM[0, 0] / Y_test.value_counts()[0] * 100:.2f} %")
print(f"Taux de mauvais classement (Classe 0 - Sans AVC): {matrice_SVM[0, 1] / Y_test.value_counts()[0] * 100:.2f} %")
print(f"Taux de bon classement (Classe 1 - Avec AVC): {matrice_SVM[1, 1] / Y_test.value_counts()[1] * 100:.2f} %")
print(f"Taux de mauvais classement (Classe 1 - Avec AVC): {matrice_SVM[1, 0] / Y_test.value_counts()[1] * 100:.2f} %")
Taux de bon classement (Classe 0 - Sans AVC): 87.72 % Taux de mauvais classement (Classe 0 - Sans AVC): 12.28 % Taux de bon classement (Classe 1 - Avec AVC): 95.86 % Taux de mauvais classement (Classe 1 - Avec AVC): 4.14 %
In [ ]:
# Courbe ROC
fpr_SVC, tpr_SVC, thresholds_SVC = roc_curve(Y_test, best_modele_SVC.predict_proba(X_test)[:, 1])
roc_auc_SVC = auc(fpr_SVC, tpr_SVC)
plt.figure()
plt.plot(fpr_SVC, tpr_SVC, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc_SVC)
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('Taux de faux positif')
plt.ylabel('Taux de vrai positif')
plt.title('Courbe de ROC pour SVM')
plt.legend(loc="lower right")
plt.show()
In [ ]:
result = permutation_importance(best_modele_SVC, X, Y, n_repeats=10)
sorted_idx = result.importances_mean.argsort()
plt.barh(X.columns[sorted_idx], result.importances_mean[sorted_idx])
plt.xlabel("Importance des caractéristiques")
plt.show()
K-plusprochesvoisins(KNN)¶
optimisation des parametres¶
In [ ]:
# Définir le modèle et les paramètres
#modele_KNN=KNeighborsClassifier()
#param_grid = [{
# 'n_neighbors': [5, 7, 9, 11],
# 'weights': ['uniform', 'distance'], # Poids uniformes ou pondérés
# 'metric':['euclidean','manhattan']
# }
#]
# Créer l'objet GridSearchCV
#modele_opt_KNN = GridSearchCV(modele_KNN,# modèle initialisé
# param_grid, # grilles de parametre du modèle
# cv=10, # cross--validation
# verbose=1 #longueur
# )
# Entrainement du modèle
#modele_opt_KNN.fit(X_train,Y_train)
# parametre optimaux
#best_param = modele_opt_KNN.best_params_
#print("VOILA LES MEILLEURS PARAMÉTRES :",best_param)
# meilleur modèle
#best_modele_KNN = modele_opt_KNN.best_estimator_
#best_modele_KNN
In [ ]:
best_modele_KNN = KNeighborsClassifier(n_neighbors=7, weights='uniform', metric='euclidean')
best_modele_KNN.fit(X_train, Y_train)
Out[ ]:
KNeighborsClassifier(metric='euclidean', n_neighbors=7)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
KNeighborsClassifier(metric='euclidean', n_neighbors=7)
In [ ]:
#best_modele_KNN = KNeighborsClassifier(n_neighbors=7, weights='uniform', metric='manhattan')
#best_modele_KNN.fit(X_train, Y_train)
Prédiction¶
In [ ]:
# Prédiction sur la base test
best_Y_test_pred_KNN = best_modele_KNN.predict(X_test)
best_Y_test_pred_KNN
Out[ ]:
array([1, 0, 0, ..., 0, 0, 0])
In [ ]:
# Prédiction sur la base train
best_Y_train_pred_KNN = best_modele_KNN.predict(X_train)
best_Y_train_pred_KNN
Out[ ]:
array([1, 1, 0, ..., 1, 0, 1])
In [ ]:
# prediction en proba sur la base test
Y_pred_train_proba_KNN =best_modele_KNN.predict_proba(X_test)
Y_pred_train_proba_KNN
Out[ ]:
array([[0.14285714, 0.85714286],
[1. , 0. ],
[1. , 0. ],
...,
[1. , 0. ],
[1. , 0. ],
[1. , 0. ]])
Mesure de performance¶
Accuracy¶
In [ ]:
# Sur la base test
Acc_test_KNN = accuracy_score(Y_test, best_Y_test_pred_KNN) * 100
Acc_train_KNN = accuracy_score(Y_train, best_Y_train_pred_KNN) * 100
# Sur la base train
print("Accuracy sur train:", Acc_train_KNN, "%")
print("Accuracy sur test:", Acc_test_KNN, "%")
Accuracy sur train: 89.33137398971344 % Accuracy sur test: 87.35001714089819 %
F1 Score¶
In [ ]:
# Sur la base test et train
F1_score_test_KNN = f1_score(Y_test, best_Y_test_pred_KNN, average='weighted') * 100
F1_score_train_KNN = f1_score(Y_train, best_Y_train_pred_KNN, average='weighted') * 100
# Sur la base train
print("F1_score sur train:", F1_score_train_KNN, "%")
print("F1_score sur test:", F1_score_test_KNN, "%")
F1_score sur train: 89.24452031458395 % F1_score sur test: 87.21720776325233 %
Précision¶
In [ ]:
# Sur la base test et train
precision_score_test_KNN = precision_score(Y_test, best_Y_test_pred_KNN, average='weighted') * 100
precision_score_train_KNN = precision_score(Y_train, best_Y_train_pred_KNN, average='weighted') * 100
# Sur la base train
print("Précision sur train:", precision_score_train_KNN, "%")
print("Précision sur test:", precision_score_test_KNN, "%")
Précision sur train: 90.60784379234124 % Précision sur test: 89.06846256454828 %
Recall¶
In [ ]:
# Sur la base test et train
recall_score_test_KNN = recall_score(Y_test, best_Y_test_pred_KNN, average='weighted') * 100
recall_score_train_KNN = recall_score(Y_train, best_Y_train_pred_KNN, average='weighted') * 100
# Sur la base train
print("Recall sur train:", recall_score_train_KNN, "%")
print("Recall sur test:", recall_score_test_KNN, "%")
Recall sur train: 89.33137398971344 % Recall sur test: 87.35001714089819 %
Matrice de confusion¶
In [ ]:
matrice_KNN = confusion_matrix(Y_test, best_Y_test_pred_SVC)
# Affichage de la matrice de confusion avec titre
fig, ax = plot_confusion_matrix(conf_mat=matrice_KNN,
show_absolute=True,
show_normed=True,
colorbar=True)
plt.title("Confusion Matrix KNN")
plt.show()
print("Alors dans la classe 0,sur",Y_test.value_counts()[0],
"individu,le modèle réussit à faire un bon classement sur", matrice_KNN[0,0],
" individu et une erreur sur", matrice_KNN[0,1], "\n Dans la classe 1,sur",Y_test.value_counts()[1],
"individus le modèle fait un bon classement sur", matrice_KNN[1,1],
" individu et une erreur sur", matrice_KNN[0,0] )
Alors dans la classe 0,sur 1466 individu,le modèle réussit à faire un bon classement sur 1286 individu et une erreur sur 180 Dans la classe 1,sur 1451 individus le modèle fait un bon classement sur 1391 individu et une erreur sur 1286
*taux de bon et de mauvais classement*
In [ ]:
print(f"Taux de bon classement (Classe 0 - Sans AVC): {matrice_KNN[0, 0] / Y_test.value_counts()[0] * 100:.2f} %")
print(f"Taux de mauvais classement (Classe 0 - Sans AVC): {matrice_KNN[0, 1] / Y_test.value_counts()[0] * 100:.2f} %")
print(f"Taux de bon classement (Classe 1 - Avec AVC): {matrice_KNN[1, 1] / Y_test.value_counts()[1] * 100:.2f} %")
print(f"Taux de mauvais classement (Classe 1 - Avec AVC): {matrice_KNN[1, 0] / Y_test.value_counts()[1] * 100:.2f} %")
Taux de bon classement (Classe 0 - Sans AVC): 87.72 % Taux de mauvais classement (Classe 0 - Sans AVC): 12.28 % Taux de bon classement (Classe 1 - Avec AVC): 95.86 % Taux de mauvais classement (Classe 1 - Avec AVC): 4.14 %
In [ ]:
# Courbe ROC
fpr_KNN, tpr_KNN, thresholds_SVC = roc_curve(Y_test, best_modele_KNN.predict_proba(X_test)[:, 1])
roc_auc_KNN = auc(fpr_KNN, tpr_KNN)
plt.figure()
plt.plot(fpr_KNN, tpr_KNN, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc_KNN)
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('Taux de faux positif')
plt.ylabel('Taux de vrai positif')
plt.title('Courbe de ROC pour KNN')
plt.legend(loc="lower right")
plt.show()
In [ ]:
result = permutation_importance(best_modele_KNN, X, Y, n_repeats=10)
sorted_idx = result.importances_mean.argsort()
plt.barh(X.columns[sorted_idx], result.importances_mean[sorted_idx])
plt.xlabel("Importance des caractéristiques")
plt.show()
Grandient Boosting¶
In [ ]:
# initialisation du modele
modele_GB = GradientBoostingClassifier()
param_grid_GB = ({
'n_estimators': [100,200],
'max_depth': [2,3,4,5],
'learning_rate': [0.1,0.2], # taux d'apprentissage
'loss': ['log_loss'], # fonction d'erreur
'subsample': [0.8, 1.0] #Stochastic GB pour réduire la variance
})
# modele optimal
modele_opt_GB = GridSearchCV(modele_GB,param_grid_GB,
cv = 5,
scoring='roc_auc')
Entrainement du modèle¶
In [ ]:
modele_opt_GB.fit(X_train,Y_train)
Out[ ]:
GridSearchCV(cv=5, estimator=GradientBoostingClassifier(),
param_grid={'learning_rate': [0.1, 0.2], 'loss': ['log_loss'],
'max_depth': [2, 3, 4, 5], 'n_estimators': [100, 200],
'subsample': [0.8, 1.0]},
scoring='roc_auc')In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
GridSearchCV(cv=5, estimator=GradientBoostingClassifier(),
param_grid={'learning_rate': [0.1, 0.2], 'loss': ['log_loss'],
'max_depth': [2, 3, 4, 5], 'n_estimators': [100, 200],
'subsample': [0.8, 1.0]},
scoring='roc_auc')GradientBoostingClassifier(learning_rate=0.2, max_depth=5, n_estimators=200,
subsample=0.8)GradientBoostingClassifier(learning_rate=0.2, max_depth=5, n_estimators=200,
subsample=0.8)In [ ]:
# parametres optimaux
best_param_GB = modele_opt_GB.best_params_
print("VOILA LES MEILLEURS PARAMÉTRES :",best_param_GB)
# best modele
best_modele_GB = modele_opt_GB.best_estimator_
best_modele_GB
VOILA LES MEILLEURS PARAMÉTRES : {'learning_rate': 0.2, 'loss': 'log_loss', 'max_depth': 5, 'n_estimators': 200, 'subsample': 0.8}
Out[ ]:
GradientBoostingClassifier(learning_rate=0.2, max_depth=5, n_estimators=200,
subsample=0.8)In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
GradientBoostingClassifier(learning_rate=0.2, max_depth=5, n_estimators=200,
subsample=0.8)Prédiction¶
In [ ]:
best_Y_test_pred_GB = best_modele_GB.predict(X_test)
best_Y_test_pred_GB
Out[ ]:
array([1, 0, 0, ..., 0, 0, 0])
In [ ]:
best_Y_train_pred_GB = best_modele_GB.predict(X_train)
best_Y_train_pred_GB
Out[ ]:
array([1, 1, 0, ..., 1, 0, 1])
Mesure de performance¶
Accuracy¶
In [ ]:
# Sur la base test
Acc_test_GB = accuracy_score(Y_test, best_Y_test_pred_GB) * 100
Acc_train_GB = accuracy_score(Y_train, best_Y_train_pred_GB) * 100
# Sur la base train
print("Accuracy sur train:", Acc_train_GB, "%")
print("Accuracy sur test:", Acc_test_GB, "%")
Accuracy sur train: 99.85304922850845 % Accuracy sur test: 95.33767569420638 %
F1 score¶
In [ ]:
# Sur la base test et train
F1_score_test_GB = f1_score(Y_test, best_Y_test_pred_GB, average='weighted') * 100
F1_score_train_GB = f1_score(Y_train, best_Y_train_pred_GB, average='weighted') * 100
# Sur la base train
print("F1_score sur train:", F1_score_train_GB, "%")
print("F1_score sur test:", F1_score_test_GB, "%")
F1_score sur train: 99.85304936813544 % F1_score sur test: 95.33589401841132 %
Précision¶
In [ ]:
# Sur la base test et train
precision_score_test_GB = precision_score(Y_test, best_Y_test_pred_GB, average='weighted') * 100
precision_score_train_GB = precision_score(Y_train, best_Y_train_pred_GB, average='weighted') * 100
# Sur la base train
print("Précision sur train:", precision_score_train_GB, "%")
print("Précision sur test:", precision_score_test_GB, "%")
Précision sur train: 99.85311850878198 % Précision sur test: 95.39021817593391 %
Recall¶
In [ ]:
# Sur la base test et train
recall_score_test_GB = recall_score(Y_test, best_Y_test_pred_GB, average='weighted') * 100
recall_score_train_GB = recall_score(Y_train, best_Y_train_pred_GB, average='weighted') * 100
# Sur la base train
print("Recall sur train:", recall_score_train_GB, "%")
print("Recall sur test:", recall_score_test_GB, "%")
Recall sur train: 99.85304922850845 % Recall sur test: 95.33767569420638 %
Matrice de confusion¶
In [ ]:
matrice_GB = confusion_matrix(Y_test, best_Y_test_pred_SVC)
# Affichage de la matrice de confusion avec titre
fig, ax = plot_confusion_matrix(conf_mat=matrice_GB,
show_absolute=True,
show_normed=True,
colorbar=True)
plt.title("Confusion Matrix GB")
plt.show()
print("Alors dans la classe 0,sur",Y_test.value_counts()[0],
"individu,le modèle réussit à faire un bon classement sur", matrice_GB[0,0],
" individu et une erreur sur", matrice_GB[0,1], "\n Dans la classe 1,sur",Y_test.value_counts()[1],
"individus le modèle fait un bon classement sur", matrice_GB[1,1],
" individu et une erreur sur", matrice_GB[0,0] )
Alors dans la classe 0,sur 1466 individu,le modèle réussit à faire un bon classement sur 1286 individu et une erreur sur 180 Dans la classe 1,sur 1451 individus le modèle fait un bon classement sur 1391 individu et une erreur sur 1286
*taux de bon et de mauvais classement*
In [ ]:
print(f"Taux de bon classement (Classe 0 - Sans AVC): {matrice_GB[0, 0] / Y_test.value_counts()[0] * 100:.2f} %")
print(f"Taux de mauvais classement (Classe 0 - Sans AVC): {matrice_GB[0, 1] / Y_test.value_counts()[0] * 100:.2f} %")
print(f"Taux de bon classement (Classe 1 - Avec AVC): {matrice_GB[1, 1] / Y_test.value_counts()[1] * 100:.2f} %")
print(f"Taux de mauvais classement (Classe 1 - Avec AVC): {matrice_GB[1, 0] / Y_test.value_counts()[1] * 100:.2f} %")
Taux de bon classement (Classe 0 - Sans AVC): 87.72 % Taux de mauvais classement (Classe 0 - Sans AVC): 12.28 % Taux de bon classement (Classe 1 - Avec AVC): 95.86 % Taux de mauvais classement (Classe 1 - Avec AVC): 4.14 %
In [ ]:
# Courbe ROC
fpr_GB, tpr_GB, thresholds_SVC = roc_curve(Y_test, best_modele_GB.predict_proba(X_test)[:, 1])
roc_auc_GB = auc(fpr_GB, tpr_GB)
plt.figure()
plt.plot(fpr_GB, tpr_GB, color='darkorange', lw=2, label='ROC curve (area = %0.2f)' % roc_auc_GB)
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('Taux de faux positif')
plt.ylabel('Taux de vrai positif')
plt.title('Courbe de ROC pour Grandient Boosting')
plt.legend(loc="lower right")
plt.show()
In [ ]:
from sklearn.model_selection import learning_curve
import matplotlib.pyplot as plt
import numpy as np
train_sizes, train_scores, val_scores = learning_curve(
estimator=best_modele_GB, X=X_train, y=Y_train, cv=5,
scoring="roc_auc", train_sizes=np.linspace(0.1, 1.0, 10)
)
plt.plot(train_sizes, np.mean(train_scores, axis=1), label="Train")
plt.plot(train_sizes, np.mean(val_scores, axis=1), label="Validation")
plt.xlabel("Training Set Size")
plt.ylabel("AUC Score")
plt.legend()
plt.show()
In [ ]:
result = permutation_importance(best_modele_GB, X, Y, n_repeats=10)
sorted_idx = result.importances_mean.argsort()
plt.barh(X.columns[sorted_idx], result.importances_mean[sorted_idx])
plt.xlabel("Importance des caractéristiques")
plt.show()
Réseau de Neurone¶
In [ ]:
#from tensorflow.keras.models import Sequential
#from tensorflow.keras.layers import Dense, Dropout, BatchNormalization
#from tensorflow.keras.optimizers import Adam
#from tensorflow.keras.callbacks import EarlyStopping
#model_base = Sequential([
# Dense(128, activation='relu', input_shape=(X_train.shape[1],)),
# BatchNormalization(),
# Dropout(0.3), # Réduit le surajustement
# Dense(64, activation='relu'),
# BatchNormalization(),
# Dropout(0.2),
# Dense(32, activation='relu'),
# BatchNormalization(),
# Dense(1, activation='sigmoid')
#])
*Création d'un modèle de base avec 4 couches cachées*
In [ ]:
# Conversion en float32
X_train = X_train.astype(np.float32)
Y_train = Y_train.astype(np.float32)
X_test = X_test.astype(np.float32)
Y_test = Y_test.astype(np.float32)
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, BatchNormalization
from tensorflow.keras.optimizers import Adam
model_base = Sequential([
Dense(128, input_shape=(X_train.shape[1],), activation='relu'),
BatchNormalization(),
Dropout(0.2),
Dense(64, activation='relu'),
BatchNormalization(),
Dropout(0.2),
Dense(64, activation='relu'),
BatchNormalization(),
Dropout(0.2),
Dense(32, activation='relu'),
BatchNormalization(),
Dropout(0.2),
Dense(32, activation='relu'),
BatchNormalization(),
Dropout(0.2),
Dense(1, activation='sigmoid')
])
# Construction et entraînement du modèle
model_base.compile(
optimizer=Adam(learning_rate=0.001),
loss='binary_crossentropy',
metrics=['accuracy'])
history_base = model_base.fit(
X_train, Y_train,
validation_data=(X_test, Y_test),
epochs=100,
batch_size=32,
verbose=1
)
/usr/local/lib/python3.11/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(activity_regularizer=activity_regularizer, **kwargs)
Epoch 1/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 9s 7ms/step - accuracy: 0.6901 - loss: 0.6063 - val_accuracy: 0.7875 - val_loss: 0.4693 Epoch 2/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 7ms/step - accuracy: 0.7593 - loss: 0.5027 - val_accuracy: 0.8180 - val_loss: 0.4128 Epoch 3/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.7722 - loss: 0.4708 - val_accuracy: 0.8204 - val_loss: 0.3989 Epoch 4/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.7869 - loss: 0.4574 - val_accuracy: 0.8228 - val_loss: 0.3887 Epoch 5/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.7852 - loss: 0.4549 - val_accuracy: 0.8348 - val_loss: 0.3730 Epoch 6/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.8071 - loss: 0.4179 - val_accuracy: 0.8433 - val_loss: 0.3693 Epoch 7/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.8005 - loss: 0.4265 - val_accuracy: 0.8430 - val_loss: 0.3644 Epoch 8/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8187 - loss: 0.4173 - val_accuracy: 0.8375 - val_loss: 0.3623 Epoch 9/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 7ms/step - accuracy: 0.8068 - loss: 0.4169 - val_accuracy: 0.8450 - val_loss: 0.3541 Epoch 10/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8130 - loss: 0.4147 - val_accuracy: 0.8533 - val_loss: 0.3530 Epoch 11/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 3s 7ms/step - accuracy: 0.8273 - loss: 0.3957 - val_accuracy: 0.8570 - val_loss: 0.3439 Epoch 12/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8294 - loss: 0.3917 - val_accuracy: 0.8557 - val_loss: 0.3402 Epoch 13/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.8300 - loss: 0.3867 - val_accuracy: 0.8481 - val_loss: 0.3467 Epoch 14/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8225 - loss: 0.3950 - val_accuracy: 0.8519 - val_loss: 0.3451 Epoch 15/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 3s 7ms/step - accuracy: 0.8250 - loss: 0.3866 - val_accuracy: 0.8468 - val_loss: 0.3468 Epoch 16/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.8205 - loss: 0.3985 - val_accuracy: 0.8557 - val_loss: 0.3343 Epoch 17/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8281 - loss: 0.3837 - val_accuracy: 0.8560 - val_loss: 0.3348 Epoch 18/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8348 - loss: 0.3784 - val_accuracy: 0.8598 - val_loss: 0.3309 Epoch 19/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 7ms/step - accuracy: 0.8324 - loss: 0.3787 - val_accuracy: 0.8560 - val_loss: 0.3339 Epoch 20/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.8288 - loss: 0.3800 - val_accuracy: 0.8632 - val_loss: 0.3265 Epoch 21/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.8355 - loss: 0.3715 - val_accuracy: 0.8718 - val_loss: 0.3221 Epoch 22/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.8397 - loss: 0.3690 - val_accuracy: 0.8646 - val_loss: 0.3303 Epoch 23/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 3s 7ms/step - accuracy: 0.8404 - loss: 0.3667 - val_accuracy: 0.8570 - val_loss: 0.3305 Epoch 24/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.8375 - loss: 0.3792 - val_accuracy: 0.8697 - val_loss: 0.3165 Epoch 25/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8537 - loss: 0.3552 - val_accuracy: 0.8673 - val_loss: 0.3181 Epoch 26/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.8407 - loss: 0.3657 - val_accuracy: 0.8684 - val_loss: 0.3209 Epoch 27/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.8475 - loss: 0.3569 - val_accuracy: 0.8684 - val_loss: 0.3205 Epoch 28/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.8519 - loss: 0.3474 - val_accuracy: 0.8680 - val_loss: 0.3218 Epoch 29/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8405 - loss: 0.3628 - val_accuracy: 0.8684 - val_loss: 0.3256 Epoch 30/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8457 - loss: 0.3596 - val_accuracy: 0.8636 - val_loss: 0.3167 Epoch 31/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 5ms/step - accuracy: 0.8523 - loss: 0.3501 - val_accuracy: 0.8752 - val_loss: 0.3130 Epoch 32/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8567 - loss: 0.3403 - val_accuracy: 0.8745 - val_loss: 0.3155 Epoch 33/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8557 - loss: 0.3401 - val_accuracy: 0.8749 - val_loss: 0.3105 Epoch 34/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8512 - loss: 0.3563 - val_accuracy: 0.8697 - val_loss: 0.3142 Epoch 35/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 3s 8ms/step - accuracy: 0.8470 - loss: 0.3537 - val_accuracy: 0.8636 - val_loss: 0.3243 Epoch 36/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.8481 - loss: 0.3528 - val_accuracy: 0.8749 - val_loss: 0.3086 Epoch 37/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 3s 7ms/step - accuracy: 0.8583 - loss: 0.3386 - val_accuracy: 0.8745 - val_loss: 0.3071 Epoch 38/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8542 - loss: 0.3459 - val_accuracy: 0.8821 - val_loss: 0.3042 Epoch 39/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8451 - loss: 0.3491 - val_accuracy: 0.8807 - val_loss: 0.3022 Epoch 40/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8487 - loss: 0.3480 - val_accuracy: 0.8711 - val_loss: 0.3073 Epoch 41/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8561 - loss: 0.3377 - val_accuracy: 0.8790 - val_loss: 0.3053 Epoch 42/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.8585 - loss: 0.3366 - val_accuracy: 0.8735 - val_loss: 0.3101 Epoch 43/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 7ms/step - accuracy: 0.8530 - loss: 0.3350 - val_accuracy: 0.8797 - val_loss: 0.3009 Epoch 44/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 3s 6ms/step - accuracy: 0.8626 - loss: 0.3360 - val_accuracy: 0.8903 - val_loss: 0.2932 Epoch 45/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8617 - loss: 0.3225 - val_accuracy: 0.8855 - val_loss: 0.2974 Epoch 46/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8551 - loss: 0.3407 - val_accuracy: 0.8786 - val_loss: 0.3010 Epoch 47/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8543 - loss: 0.3421 - val_accuracy: 0.8852 - val_loss: 0.2987 Epoch 48/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8585 - loss: 0.3354 - val_accuracy: 0.8855 - val_loss: 0.2978 Epoch 49/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8701 - loss: 0.3177 - val_accuracy: 0.8738 - val_loss: 0.3036 Epoch 50/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 3s 9ms/step - accuracy: 0.8580 - loss: 0.3326 - val_accuracy: 0.8780 - val_loss: 0.2982 Epoch 51/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8697 - loss: 0.3176 - val_accuracy: 0.8828 - val_loss: 0.2976 Epoch 52/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8676 - loss: 0.3198 - val_accuracy: 0.8848 - val_loss: 0.2955 Epoch 53/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 5ms/step - accuracy: 0.8609 - loss: 0.3321 - val_accuracy: 0.8845 - val_loss: 0.2940 Epoch 54/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8568 - loss: 0.3351 - val_accuracy: 0.8738 - val_loss: 0.3049 Epoch 55/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 3s 6ms/step - accuracy: 0.8680 - loss: 0.3098 - val_accuracy: 0.8862 - val_loss: 0.2908 Epoch 56/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8613 - loss: 0.3348 - val_accuracy: 0.8896 - val_loss: 0.2923 Epoch 57/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.8685 - loss: 0.3119 - val_accuracy: 0.8769 - val_loss: 0.3006 Epoch 58/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 8ms/step - accuracy: 0.8663 - loss: 0.3155 - val_accuracy: 0.8845 - val_loss: 0.2921 Epoch 59/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.8632 - loss: 0.3367 - val_accuracy: 0.8869 - val_loss: 0.2957 Epoch 60/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 3s 7ms/step - accuracy: 0.8673 - loss: 0.3137 - val_accuracy: 0.8855 - val_loss: 0.2898 Epoch 61/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8695 - loss: 0.3160 - val_accuracy: 0.8821 - val_loss: 0.2957 Epoch 62/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8653 - loss: 0.3215 - val_accuracy: 0.8882 - val_loss: 0.2810 Epoch 63/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 7ms/step - accuracy: 0.8615 - loss: 0.3269 - val_accuracy: 0.8831 - val_loss: 0.2941 Epoch 64/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 3s 8ms/step - accuracy: 0.8636 - loss: 0.3249 - val_accuracy: 0.8756 - val_loss: 0.3012 Epoch 65/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 7ms/step - accuracy: 0.8657 - loss: 0.3257 - val_accuracy: 0.8910 - val_loss: 0.2890 Epoch 66/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 3s 7ms/step - accuracy: 0.8560 - loss: 0.3248 - val_accuracy: 0.8858 - val_loss: 0.2915 Epoch 67/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 5ms/step - accuracy: 0.8645 - loss: 0.3202 - val_accuracy: 0.8869 - val_loss: 0.2859 Epoch 68/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8766 - loss: 0.3059 - val_accuracy: 0.8862 - val_loss: 0.2871 Epoch 69/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 7ms/step - accuracy: 0.8687 - loss: 0.3117 - val_accuracy: 0.8797 - val_loss: 0.2998 Epoch 70/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 3s 8ms/step - accuracy: 0.8711 - loss: 0.3185 - val_accuracy: 0.8900 - val_loss: 0.2868 Epoch 71/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 9ms/step - accuracy: 0.8751 - loss: 0.3114 - val_accuracy: 0.8848 - val_loss: 0.2913 Epoch 72/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 7ms/step - accuracy: 0.8728 - loss: 0.3130 - val_accuracy: 0.8800 - val_loss: 0.2977 Epoch 73/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 5ms/step - accuracy: 0.8707 - loss: 0.3094 - val_accuracy: 0.8759 - val_loss: 0.2985 Epoch 74/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8644 - loss: 0.3161 - val_accuracy: 0.8917 - val_loss: 0.2804 Epoch 75/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 7ms/step - accuracy: 0.8786 - loss: 0.2985 - val_accuracy: 0.8920 - val_loss: 0.2827 Epoch 76/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 7ms/step - accuracy: 0.8762 - loss: 0.3097 - val_accuracy: 0.8804 - val_loss: 0.2896 Epoch 77/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 7ms/step - accuracy: 0.8677 - loss: 0.3183 - val_accuracy: 0.8920 - val_loss: 0.2869 Epoch 78/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 3s 7ms/step - accuracy: 0.8714 - loss: 0.3070 - val_accuracy: 0.8845 - val_loss: 0.2865 Epoch 79/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8678 - loss: 0.3172 - val_accuracy: 0.8951 - val_loss: 0.2744 Epoch 80/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8588 - loss: 0.3322 - val_accuracy: 0.8906 - val_loss: 0.2826 Epoch 81/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8785 - loss: 0.3101 - val_accuracy: 0.8848 - val_loss: 0.2887 Epoch 82/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 7ms/step - accuracy: 0.8774 - loss: 0.3000 - val_accuracy: 0.8903 - val_loss: 0.2885 Epoch 83/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.8656 - loss: 0.3235 - val_accuracy: 0.8903 - val_loss: 0.2847 Epoch 84/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8749 - loss: 0.3060 - val_accuracy: 0.8882 - val_loss: 0.2830 Epoch 85/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 9ms/step - accuracy: 0.8776 - loss: 0.3031 - val_accuracy: 0.8780 - val_loss: 0.2921 Epoch 86/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 6ms/step - accuracy: 0.8695 - loss: 0.3099 - val_accuracy: 0.8828 - val_loss: 0.2870 Epoch 87/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 3s 7ms/step - accuracy: 0.8709 - loss: 0.3142 - val_accuracy: 0.8872 - val_loss: 0.2790 Epoch 88/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 5ms/step - accuracy: 0.8771 - loss: 0.2898 - val_accuracy: 0.8838 - val_loss: 0.2832 Epoch 89/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 5ms/step - accuracy: 0.8759 - loss: 0.3009 - val_accuracy: 0.8937 - val_loss: 0.2798 Epoch 90/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8733 - loss: 0.3139 - val_accuracy: 0.8900 - val_loss: 0.2801 Epoch 91/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 3s 6ms/step - accuracy: 0.8879 - loss: 0.2818 - val_accuracy: 0.8886 - val_loss: 0.2845 Epoch 92/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 3s 7ms/step - accuracy: 0.8688 - loss: 0.3092 - val_accuracy: 0.8786 - val_loss: 0.2915 Epoch 93/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8737 - loss: 0.3156 - val_accuracy: 0.8903 - val_loss: 0.2825 Epoch 94/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8789 - loss: 0.2964 - val_accuracy: 0.8831 - val_loss: 0.2883 Epoch 95/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8764 - loss: 0.2973 - val_accuracy: 0.8797 - val_loss: 0.2889 Epoch 96/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8774 - loss: 0.3014 - val_accuracy: 0.8879 - val_loss: 0.2801 Epoch 97/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8668 - loss: 0.3228 - val_accuracy: 0.8924 - val_loss: 0.2753 Epoch 98/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step - accuracy: 0.8889 - loss: 0.2796 - val_accuracy: 0.8893 - val_loss: 0.2820 Epoch 99/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 3s 8ms/step - accuracy: 0.8682 - loss: 0.3181 - val_accuracy: 0.8865 - val_loss: 0.2845 Epoch 100/100 213/213 ━━━━━━━━━━━━━━━━━━━━ 2s 7ms/step - accuracy: 0.8852 - loss: 0.2909 - val_accuracy: 0.8903 - val_loss: 0.2777
Prédiction
In [ ]:
# Obtenir les prédictions
y_train_pred_prob = model_base.predict(X_train)
y_test_pred_prob = model_base.predict(X_test)
# Convertir les probabilités en classes (0 ou 1)
y_train_pred = (y_train_pred_prob > 0.5).astype(int)
y_test_pred = (y_test_pred_prob > 0.5).astype(int)
213/213 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step 92/92 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step
Mesure de performance
Accuracy
In [ ]:
train_accuracy = accuracy_score(Y_train, y_train_pred)* 100
test_accuracy = accuracy_score(Y_test, y_test_pred)* 100
print(f"Accuracy sur la base train: {train_accuracy:.4f}")
print(f"Accuracy sur la base test: {test_accuracy:.4f}")
Accuracy sur la base train: 90.9037 Accuracy sur la base test: 89.0298
Précision
In [ ]:
train_precision = precision_score(Y_train, y_train_pred)* 100
test_precision = precision_score(Y_test, y_test_pred)* 100
print(f"Precision sur la base train: {train_precision:.4f}")
print(f"Precision sur la base test: {test_precision:.4f}")
Precision sur la base train: 85.2845 Precision sur la base test: 83.5608
F1-score
In [ ]:
train_f1 = f1_score(Y_train, y_train_pred)* 100
test_f1 = f1_score(Y_test, y_test_pred)* 100
print(f"F1-score sur la base train: {train_f1:.4f}")
print(f"F1-score sur la base test: {test_f1:.4f}")
F1-score sur la base train: 91.5954 F1-score sur la base test: 89.7959
Recall
In [ ]:
train_recall = recall_score(Y_train, y_train_pred)* 100
test_recall = recall_score(Y_test, y_test_pred)* 100
print(f"Recall sur la base train: {train_recall:.4f}")
print(f"Recall sur la base test: {test_recall:.4f}")
Recall sur la base train: 98.9150 Recall sur la base test: 97.0365
Matrice de confusion
In [ ]:
# Matrice de confusion pour le test
matrice_RN = confusion_matrix(Y_test, y_test_pred)
plt.figure(figsize=(8, 6))
sns.heatmap(matrice_RN, annot=True, fmt='d', cmap='Blues',
xticklabels=['Prédit 0', 'Prédit 1'],
yticklabels=['Vrai 0', 'Vrai 1'])
plt.title('Matrice de confusion - Réseau de neurone')
plt.ylabel('Vraie classe')
plt.xlabel('Classe prédite')
plt.show()
Taux de bon classement
In [ ]:
print(f"Taux de bon classement (Classe 0 - Sans AVC): {matrice_RN[0, 0] / Y_test.value_counts()[0] * 100:.2f} %")
print(f"Taux de mauvais classement (Classe 0 - Sans AVC): {matrice_RN[0, 1] / Y_test.value_counts()[0] * 100:.2f} %")
print(f"Taux de bon classement (Classe 1 - Avec AVC): {matrice_RN[1, 1] / Y_test.value_counts()[1] * 100:.2f} %")
print(f"Taux de mauvais classement (Classe 1 - Avec AVC): {matrice_RN[1, 0] / Y_test.value_counts()[1] * 100:.2f} %")
Taux de bon classement (Classe 0 - Sans AVC): 81.11 % Taux de mauvais classement (Classe 0 - Sans AVC): 18.89 % Taux de bon classement (Classe 1 - Avec AVC): 97.04 % Taux de mauvais classement (Classe 1 - Avec AVC): 2.96 %
Courbe de ROC
In [ ]:
# Calcul des métriques ROC
from sklearn.metrics import roc_curve, roc_auc_score
fpr, tpr, thresholds = roc_curve(Y_test, y_test_pred_prob)
auc_score = roc_auc_score(Y_test, y_test_pred_prob)
# Visualisation comparative des courbes ROC
plt.figure(figsize=(8, 6))
plt.plot(fpr, tpr, color='green', lw=2, label=f'ROC curve (AUC = {auc_score:.2f})')
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('Taux de faux positifs')
plt.ylabel('Taux de vrais positifs')
plt.title('Courbes ROC')
plt.legend(loc="lower right")
plt.show()
In [ ]:
# Courbes d'apprentissage
plt.figure(figsize=(12, 4))
plt.subplot(1, 2, 1)
plt.plot(history_base.history['accuracy'], label='Train Accuracy')
plt.plot(history_base.history['val_accuracy'], label='Validation Accuracy')
plt.title('Évolution de l\'Accuracy')
plt.xlabel('Époque')
plt.ylabel('Précision')
plt.legend()
plt.subplot(1, 2, 2)
plt.plot(history_base.history['loss'], label='Train Loss')
plt.plot(history_base.history['val_loss'], label='Validation Loss')
plt.title("Courbe de Loss au cours de l'entraînement")
plt.xlabel('Époque')
plt.ylabel('Perte')
plt.legend()
plt.show()
In [ ]:
modele = ["RL","AD","RF","SVM","KNN","GB","RN"]
accuracy_scores=[Acc_test_RL,Acc_test_AD, Acc_test_RF, Acc_test_SVC,Acc_test_KNN,Acc_test_GB,test_accuracy]
matrice_test=[matrice_RL,matrice_AD,matrice_RF,matrice_SVM,matrice_KNN,matrice_GB,matrice_RN]
precision_scores=[precision_score_test_RL,precision_score_test_AD,precision_score_test_RF,
precision_score_test_SVC,precision_score_test_KNN,precision_score_test_GB, test_precision]
recall_scores=[recall_score_test_RL,recall_score_test_AD,recall_score_test_RF,recall_score_test_SVC,recall_score_test_KNN,recall_score_test_GB,test_recall]
F1_scores=[F1_score_test_RL,F1_score_test_AD,F1_score_test_RF,F1_score_test_SVC,F1_score_test_KNN,F1_score_test_GB,test_f1]
metrics_df = pd.DataFrame({
"Modèle": modele,
"Accuracy": accuracy_scores,
"Precision": precision_scores,
"Recall": recall_scores,
"F1 Score": F1_scores
})
# Arrondir pour lisibilité
metrics_df[["Accuracy", "Precision", "Recall", "F1 Score"]] = metrics_df[["Accuracy", "Precision", "Recall", "F1 Score"]].round(2)
# Triez les résultats en ordre décroissant
metrics_df = metrics_df.sort_values(by="Recall", ascending=False)
# Affichez les résultats
print(metrics_df)
Modèle Accuracy Precision Recall F1 Score 6 RN 89.03 83.56 97.04 89.80 5 GB 95.34 95.39 95.34 95.34 3 SVM 91.77 92.06 91.77 91.76 4 KNN 87.35 89.07 87.35 87.22 2 RF 87.01 87.66 87.01 86.96 1 AD 84.64 85.58 84.64 84.55 0 RL 78.03 78.19 78.03 78.00
In [ ]:
taux_bon_classement_0 = {
"Regression Logistique": matrice_RL[0, 0] / Y_test.value_counts()[0] * 100,
"Arbre de Décision": matrice_AD[0, 0] / Y_test.value_counts()[0] * 100,
"Random Forest": matrice_RF[0, 0] / Y_test.value_counts()[0] * 100,
"SVM": matrice_SVM[0, 0] / Y_test.value_counts()[0] * 100,
"KNN": matrice_KNN[0, 0] / Y_test.value_counts()[0] * 100,
"Gradient Boosting": matrice_GB[0, 0] / Y_test.value_counts()[0] * 100,
"Réseau de neurone": matrice_RN[0, 0] / Y_test.value_counts()[0] * 100
}
taux_bon_classement_1 = {
"Regression Logistique": matrice_RL[1, 1] / Y_test.value_counts()[1] * 100,
"Arbre de Décision": matrice_AD[1, 1] / Y_test.value_counts()[1] * 100,
"Random Forest": matrice_RF[1, 1] / Y_test.value_counts()[1] * 100,
"SVM": matrice_SVM[1, 1] / Y_test.value_counts()[1] * 100,
"KNN": matrice_KNN[1, 1] / Y_test.value_counts()[1] * 100,
"Gradient Boosting": matrice_GB[1, 1] / Y_test.value_counts()[1] * 100,
"Réseau de neurone": matrice_RN[1, 1] / Y_test.value_counts()[1] * 100
}
taux_mal_classement_0 = {
"Regression Logistique": matrice_RL[0, 1] / Y_test.value_counts()[0] * 100,
"Arbre de Décision": matrice_AD[0, 1] / Y_test.value_counts()[0] * 100,
"Random Forest": matrice_RF[0, 1] / Y_test.value_counts()[0] * 100,
"SVM": matrice_SVM[0, 1] / Y_test.value_counts()[0] * 100,
"KNN": matrice_KNN[0, 1] / Y_test.value_counts()[0] * 100,
"Gradient Boosting": matrice_GB[0, 1] / Y_test.value_counts()[0] * 100,
"Réseau de neurone": matrice_RN[0, 1] / Y_test.value_counts()[0] * 100
}
taux_mal_classement_1 = {
"Regression Logistique": matrice_RL[1, 0] / Y_test.value_counts()[1] * 100,
"Arbre de Décision": matrice_AD[1, 0] / Y_test.value_counts()[1] * 100,
"Random Forest": matrice_RF[1, 0] / Y_test.value_counts()[1] * 100,
"SVM": matrice_SVM[1, 0] / Y_test.value_counts()[1] * 100,
"KNN": matrice_KNN[1, 0] / Y_test.value_counts()[1] * 100,
"Gradient Boosting": matrice_GB[1, 0] / Y_test.value_counts()[1] * 100,
"Réseau de neurone": matrice_RN[1, 0] / Y_test.value_counts()[1] * 100
}
summary_classification_rates = pd.DataFrame({
"Modèle": list(taux_bon_classement_0.keys()),
"Taux Bon Classement (Classe 0)": list(taux_bon_classement_0.values()),
"Taux Mal Classement (Classe 0)": list(taux_mal_classement_0.values()),
"Taux Bon Classement (Classe 1)": list(taux_bon_classement_1.values()),
"Taux Mal Classement (Classe 1)": list(taux_mal_classement_1.values()),
})
# Arrondir pour lisibilité
summary_classification_rates[['Taux Bon Classement (Classe 0)', 'Taux Mal Classement (Classe 0)', 'Taux Bon Classement (Classe 1)', 'Taux Mal Classement (Classe 1)']] = summary_classification_rates[['Taux Bon Classement (Classe 0)', 'Taux Mal Classement (Classe 0)', 'Taux Bon Classement (Classe 1)', 'Taux Mal Classement (Classe 1)']].round(2)
print("\n--- Résumé des Taux de Classement par Modèle ---")
display(summary_classification_rates)
--- Résumé des Taux de Classement par Modèle ---
| Modèle | Taux Bon Classement (Classe 0) | Taux Mal Classement (Classe 0) | Taux Bon Classement (Classe 1) | Taux Mal Classement (Classe 1) | |
|---|---|---|---|---|---|
| 0 | Regression Logistique | 74.49 | 25.51 | 81.60 | 18.40 |
| 1 | Arbre de Décision | 76.67 | 23.33 | 92.69 | 7.31 |
| 2 | Random Forest | 80.56 | 19.44 | 93.52 | 6.48 |
| 3 | SVM | 87.72 | 12.28 | 95.86 | 4.14 |
| 4 | KNN | 87.72 | 12.28 | 95.86 | 4.14 |
| 5 | Gradient Boosting | 87.72 | 12.28 | 95.86 | 4.14 |
| 6 | Réseau de neurone | 81.11 | 18.89 | 97.04 | 2.96 |